Summary

The way that you store your data can have a huge impact on the ways that it can be practically used. For a substantial number of use cases, the optimal format for storing and querying that information is as a graph, however databases architected around that use case have historically been difficult to use at scale or for serving fast, distributed queries. In this episode Manish Jain explains how DGraph is overcoming those limitations, how the project got started, and how you can start using it today. He also discusses the various cases where a graph storage layer is beneficial, and when you would be better off using something else. In addition he talks about the challenges of building a distributed, consistent database and the tradeoffs that were made to make DGraph a reality.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. If you have ever wished that you could use the same tools for versioning and distributing your data that you use for your software then you owe it to yourself to check out what the fine folks at Quilt Data have built. Quilt is an open source platform for building a sane workflow around your data that works for your whole team, including version history, metatdata management, and flexible hosting. Stop by their booth at JupyterCon in New York City on August 22nd through the 24th to say Hi and tell them that the Data Engineering Podcast sent you! After that, keep an eye on the AWS marketplace for a pre-packaged version of Quilt for Teams to deploy into your own environment and stop fighting with your data. Python has quickly become one of the most widely used languages by both data engineers and data scientists, letting everyone on your team understand each other more easily. However, it can be tough learning it when you’re just starting out. Luckily, there’s an easy way to get involved. Written by MIT lecturer Ana Bell and published by Manning Publications, Get Programming: Learn to code with Python is the perfect way to get started working with Python. Ana’s experience

as a teacher of Python really shines through, as you get hands-on with the language without being drowned in confusing jargon or theory. Filled with practical examples and step-by-step lessons to take on, Get Programming is perfect for people who just want to get stuck in with Python. Get your copy of the book with a special 40% discount for Data Engineering Podcast listeners by going to dataengineeringpodcast.com/get-programming and use the discount code PodInit40! Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Manish Jain about DGraph, a low latency, high throughput, native and distributed graph database.

Interview

Introduction How did you get involved in the area of data management? What is DGraph and what motivated you to build it? Graph databases and graph algorithms have been part of the computing landscape for decades. What has changed in recent years to allow for the current proliferation of graph oriented storage systems?

The graph space is becoming crowded in recent years. How does DGraph compare to the current set of offerings?

What are some of the common uses of graph storage systems?

What are some potential uses that are often overlooked?

There are a few ways that graph structures and properties can be implemented, including the ability t

Summary

The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing James Meickle about his experiences building a new Airflow installation

Interview

Introduction How did you get involved in the area of data management? What was your initial project requirement?

What tooling did you consider in addition to Airflow? What aspects of the Airflow platform led you to choose it as your implementation target?

Can you describe your current deployment architecture?

How many engineers are involved in writing tasks for your Airflow installation?

What resources were the most helpful while learning about Airflow design patterns?

How have you architected your DAGs for deployment and extensibility?

What kinds of tests and automation have you put in place to support the ongoing stability of your deployment? What are some of the dead-ends or other pitfalls that you encountered during the course of this project? What aspects of Airflow have you found to be lacking that you would like to see improved? What did you wish someone had told you before you started work on your Airflow installation?

If you were to start over would you make the same choice? If Airflow wasn’t available what would be your second choice?

What are your next steps for improvements and fixes?

Contact Info

@eronarn on Twitter Website eronarn on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Quantopian Harvard Brain Science Initiative DevOps Days Boston Google Maps API Cron ETL (Extract, Transform, Load) Azkaban Luigi AWS Glue Airflow Pachyderm

Podcast Interview

AirBnB Python YAML Ansible REST (Representational State Transfer) SAML (Security Assertion Markup Language) RBAC (Role-Based Access Control) Maxime Beauchemin

Medium Blog

Celery Dask

Podcast Interview

PostgreSQL

Podcast Interview

Redis Cloudformation Jupyter Notebook Qubole Astronomer

Podcast Interview

Gunicorn Kubernetes Airflow Improvement Proposals Python Enhancement Proposals (PEP)

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

One of the longest running and most popular open source database projects is PostgreSQL. Because of its extensibility and a community focus on stability it has stayed relevant as the ecosystem of development environments and data requirements have changed and evolved over its lifetime. It is difficult to capture any single facet of this database in a single conversation, let alone the entire surface area, but in this episode Jonathan Katz does an admirable job of it. He explains how Postgres started and how it has grown over the years, highlights the fundamental features that make it such a popular choice for application developers, and the ongoing efforts to add the complex features needed by the demanding workloads of today’s data layer. To cap it off he reviews some of the exciting features that the community is working on building into future releases.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jonathan Katz about a high level view of PostgreSQL and the unique capabilities that it offers

Interview

Introduction How did you get involved in the area of data management? How did you get involved in the Postgres project? For anyone who hasn’t used it, can you describe what PostgreSQL is?

Where did Postgres get started and how has it evolved over the intervening years?

What are some of the primary characteristics of Postgres that would lead someone to choose it for a given project?

What are some cases where Postgres is the wrong choice?

What are some of the common points of confusion for new users of PostGreSQL? (particularly if they have prior database experience) The recent releases of Postgres have had some fairly substantial improvements and new features. How does the community manage to balance stability and reliability against the need to add new capabilities? What are the aspects of Postgres that allow it to remain relevant in the current landscape of rapid evolution at the data layer? Are there any plans to incorporate a distributed transaction layer into the core of the project along the lines of what has been done with Citus or CockroachDB? What is in store for the future of Postgres?

Contact Info

@jkatz05 on Twitter jkatz on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

PostgreSQL Crunchy Data Venuebook Paperless Post LAMP Stack MySQL PHP SQL ORDBMS Edgar Codd A Relational Model of Data for Large Shared Data Banks Relational Algebra Oracle DB UC Berkeley Dr. Michae

Summary

With the attention being paid to the systems that power large volumes of high velocity data it is easy to forget about the value of data collection at human scales. Ona is a company that is building technologies to support mobile data collection, analysis of the aggregated information, and user-friendly presentations. In this episode CTO Peter Lubell-Doughtie describes the architecture of the platform, the types of environments and use cases where it is being employed, and the value of small data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Peter Lubell-Doughtie about using Ona for collecting data and processing it with Canopy

Interview

Introduction How did you get involved in the area of data management? What is Ona and how did the company get started?

What are some examples of the types of customers that you work with?

What types of data do you support in your collection platform? What are some of the mechanisms that you use to ensure the accuracy of the data that is being collected by users? Does your mobile collection platform allow for anyone to submit data without having to be associated with a given account or organization? What are some of the integration challenges that are unique to the types of data that get collected by mobile field workers? Can you describe the flow of the data from collection through to analysis? To help improve the utility of the data being collected you have started building Canopy. What was the tipping point where it became worth the time and effort to start that project?

What are the architectural considerations that you factored in when designing it? What have you found to be the most challenging or unexpected aspects of building an enterprise data warehouse for general users?

What are your plans for the future of Ona and Canopy?

Contact Info

Email pld on Github Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

OpenSRP Ona Canopy Open Data Kit Earth Institute at Columbia University Sustainable Engineering Lab WHO Bill and Melinda Gates Foundation XLSForms PostGIS Kafka Druid Superset Postgres Ansible Docker Terraform

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

When working with large volumes of data that you need to access in parallel across multiple instances you need a distributed filesystem that will scale with your workload. Even better is when that same system provides multiple paradigms for interacting with the underlying storage. Ceph is a highly available, highly scalable, and performant system that has support for object storage, block storage, and native filesystem access. In this episode Sage Weil, the creator and lead maintainer of the project, discusses how it got started, how it works, and how you can start using it on your infrastructure today. He also explains where it fits in the current landscape of distributed storage and the plans for future improvements.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Sage Weil about Ceph, an open source distributed file system that supports block storage, object storage, and a file system interface.

Interview

Introduction How did you get involved in the area of data management? Can you start with an overview of what Ceph is?

What was the motivation for starting the project? What are some of the most common use cases for Ceph?

There are a large variety of distributed file systems. How would you characterize Ceph as it compares to other options (e.g. HDFS, GlusterFS, LionFS, SeaweedFS, etc.)? Given that there is no single point of failure, what mechanisms do you use to mitigate the impact of network partitions?

What mechanisms are available to ensure data integrity across the cluster?

How is Ceph implemented and how has the design evolved over time? What is required to deploy and manage a Ceph cluster?

What are the scaling factors for a cluster? What are the limitations?

How does Ceph handle mixed write workloads with either a high volume of small files or a smaller volume of larger files? In services such as S3 the data is segregated from block storage options like EBS or EFS. Since Ceph provides all of those interfaces in one project is it possible to use each of those interfaces to the same data objects in a Ceph cluster? In what situations would you advise someone against using Ceph? What are some of the most interested, unexpected, or challenging aspects of working with Ceph and the community? What are some of the plans that you have for the future of Ceph?

Contact Info

Email @liewegas on Twitter liewegas on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Ceph Red Hat DreamHo

Summary

Data integration and routing is a constantly evolving problem and one that is fraught with edge cases and complicated requirements. The Apache NiFi project models this problem as a collection of data flows that are created through a self-service graphical interface. This framework provides a flexible platform for building a wide variety of integrations that can be managed and scaled easily to fit your particular needs. In this episode project members Kevin Doran and Andy LoPresto discuss the ways that NiFi can be used, how to start using it in your environment, and plans for future development. They also explained how it fits in the broad landscape of data tools, the interesting and challenging aspects of the project, and how to build new extensions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Doran and Andy LoPresto about Apache NiFi

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what NiFi is? What is the motivation for building a GUI as the primary interface for the tool when the current trend is to represent everything as code? How did you get involved with the project?

Where does it sit in the broader landscape of data tools?

Does the data that is processed by NiFi flow through the servers that it is running on (á la Spark/Flink/Kafka), or does it orchestrate actions on other systems (á la Airflow/Oozie)?

How do you manage versioning and backup of data flows, as well as promoting them between environments?

One of the advertised features is tracking provenance for data flows that are managed by NiFi. How is that data collected and managed?

What types of reporting are available across this information?

What are some of the use cases or requirements that lend themselves well to being solved by NiFi?

When is NiFi the wrong choice?

What is involved in deploying and scaling a NiFi installation?

What are some of the system/network parameters that should be considered? What are the scaling limitations?

What have you found to be some of the most interesting, unexpected, and/or challenging aspects of building and maintaining the NiFi project and community? What do you have planned for the future of NiFi?

Contact Info

Kevin Doran

@kevdoran on Twitter Email

Andy LoPresto

@yolopey on Twitter Email

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

NiFi HortonWorks DataFlow HortonWorks Apache Software Foundation Apple CSV XML JSON Perl Python Internet Scale Asset Management Documentum DataFlow NSA (National Security Agency) 24 (TV Show) Technology Transfer Program Agile Software Development Waterfall Spark Flink Kafka Oozie Luigi Airflow FluentD ETL (Extract, Transform, and Load) ESB (Enterprise Service Bus) MiNiFi Java C++ Provenance Kubernetes Apache Atlas Data Governance Kibana K-Nearest Neighbors DevOps DSL (Domain Specific Language) NiFi Registry Artifact Repository Nexus NiFi CLI Maven Archetype IoT Docker Backpressure NiFi Wiki TLS (Transport Layer Security) Mozilla TLS Observatory NiFi Flow Design System Data Lineage GDPR (General Data Protection Regulation)

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Data is often messy or incomplete, requiring human intervention to make sense of it before being usable as input to machine learning projects. This is problematic when the volume scales beyond a handful of records. In this episode Dr. Cheryl Martin, Chief Data Scientist for Alegion, discusses the importance of properly labeled information for machine learning and artificial intelligence projects, the systems that they have built to scale the process of incorporating human intelligence in the data preparation process, and the challenges inherent to such an endeavor.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Cheryl Martin, chief data scientist at Alegion, about data labelling at scale

Interview

Introduction How did you get involved in the area of data management? To start, can you explain the problem space that Alegion is targeting and how you operate? When is it necessary to include human intelligence as part of the data lifecycle for ML/AI projects? What are some of the biggest challenges associated with managing human input to data sets intended for machine usage? For someone who is acting as human-intelligence provider as part of the workforce, what does their workflow look like?

What tools and processes do you have in place to ensure the accuracy of their inputs? How do you prevent bad actors from contributing data that would compromise the trained model?

What are the limitations of crowd-sourced data labels?

When is it beneficial to incorporate domain experts in the process?

When doing data collection from various sources, how do you ensure that intellectual property rights are respected? How do you determine the taxonomies to be used for structuring data sets that are collected, labeled or enriched for your customers?

What kinds of metadata do you track and how is that recorded/transmitted?

Do you think that human intelligence will be a necessary piece of ML/AI forever?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Alegion University of Texas at Austin Cognitive Science Labeled Data Mechanical Turk Computer Vision Sentiment Analysis Speech Recognition Taxonomy Feature Engineering

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Collaboration, distribution, and installation of software projects is largely a solved problem, but the same cannot be said of data. Every data team has a bespoke means of sharing data sets, versioning them, tracking related metadata and changes, and publishing them for use in the software systems that rely on them. The CEO and founder of Quilt Data, Kevin Moore, was sufficiently frustrated by this problem to create a platform that attempts to be the means by which data can be as collaborative and easy to work with as GitHub and your favorite programming language. In this episode he explains how the project came to be, how it works, and the many ways that you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Moore about Quilt Data, a platform and tooling for packaging, distributing, and versioning data

Interview

Introduction How did you get involved in the area of data management? What is the intended use case for Quilt and how did the project get started? Can you step through a typical workflow of someone using Quilt?

How does that change as you go from a single user to a team of data engineers and data scientists?

Can you describe the elements of what a data package consists of?

What was your criteria for the file formats that you chose?

How is Quilt architected and what have been the most significant changes or evolutions since you first started? How is the data registry implemented?

What are the limitations or edge cases that you have run into? What optimizations have you made to accelerate synchronization of the data to and from the repository?

What are the limitations in terms of data volume, format, or usage? What is your goal with the business that you have built around the project? What are your plans for the future of Quilt?

Contact Info

Email LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Quilt Data GitHub Jobs Reproducible Data Dependencies in Jupyter Reproducible Machine Learning with Jupyter and Quilt Allen Institute: Programmatic Data Access with Quilt Quilt Example: MissingNo Oracle Pandas Jupyter Ycombinator Data.World

Podcast Episode with CTO Bryon Jacob

Kaggle Parquet HDF5 Arrow PySpark Excel Scala Binder Merkle Tree Allen Institute for Cell Science Flask PostGreSQL Docker Airflow Quilt Teams Hive Hive Metastore PrestoDB

Podcast Episode

Netflix Iceberg Kubernetes Helm

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Web and mobile analytics are an important part of any business, and difficult to get right. The most frustrating part is when you realize that you haven’t been tracking a key interaction, having to write custom logic to add that event, and then waiting to collect data. Heap is a platform that automatically tracks every event so that you can retroactively decide which actions are important to your business and easily build reports with or without SQL. In this episode Dan Robinson, CTO of Heap, describes how they have architected their data infrastructure, how they build their tracking agents, and the data virtualization layer that enables users to define their own labels.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Dan Robinson about Heap and their approach to collecting, storing, and analyzing large volumes of data

Interview

Introduction How did you get involved in the area of data management? Can you start by giving a brief overview of Heap? One of your differentiating features is the fact that you capture every interaction on web and mobile platforms for your customers. How do you prevent the user experience from suffering as a result of network congestion, while ensuring the reliable delivery of that data? Can you walk through the lifecycle of a single event from source to destination and the infrastructure components that it traverses to get there? Data collected in a user’s browser can often be messy due to various browser plugins, variations in runtime capabilities, etc. How do you ensure the integrity and accuracy of that information?

What are some of the difficulties that you have faced in establishing a representation of events that allows for uniform processing and storage?

What is your approach for merging and enriching event data with the information that you retrieve from your supported integrations?

What challenges does that pose in your processing architecture?

What are some of the problems that you have had to deal with to allow for processing and storing such large volumes of data?

How has that architecture changed or evolved over the life of the company? What are some changes that you are anticipating in the near future?

Can you describe your approach for synchronizing customer data with their individual Redshift instances and the difficulties that entails? What are some of the most interesting challenges that you have faced while building the technical and business aspects of Heap? What changes have been necessary as a result of GDPR? What are your plans for the future of Heap?

Contact Info

@danlovesproofs on twitter [email protected] @drob on github heapanalytics.com / @heap on twitter https://heapanalytics.com/blog/category/engineering?utm_source=rss&utm_medium=rss

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data manageme

Summary

With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Peter Mattis about CockroachDB, the SQL database for global cloud services

Interview

Introduction How did you get involved in the area of data management? What was the motivation for creating CockroachDB and building a business around it? Can you describe the architecture of CockroachDB and how it supports distributed ACID transactions?

What are some of the tradeoffs that are necessary to allow for georeplicated data with distributed transactions? What are some of the problems that you have had to work around in the RAFT protocol to provide reliable operation of the clustering mechanism?

Go is an unconventional language for building a database. What are the pros and cons of that choice? What are some of the common points of confusion that users of CockroachDB have when operating or interacting with it?

What are the edge cases and failure modes that users should be aware of?

I know that your SQL syntax is PostGreSQL compatible, so is it possible to use existing ORMs unmodified with CockroachDB?

What are some examples of extensions that are specific to CockroachDB?

What are some of the most interesting uses of CockroachDB that you have seen? When is CockroachDB the wrong choice? What do you have planned for the future of CockroachDB?

Contact Info

Peter

LinkedIn petermattis on GitHub @petermattis on Twitter

Cockroach Labs

@CockroackDB on Twitter Website cockroachdb on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

CockroachDB Cockroach Labs SQL Google Bigtable Spanner NoSQL RDBMS (Relational Database Management System) “Big Iron” (colloquial term for mainframe computers) RAFT Consensus Algorithm Consensus MVCC (Multiversion Concurrency Control) Isolation Etcd GDPR Golang C++ Garbage Collection Metaprogramming Rust Static Linking Docker Kubernetes CAP Theorem PostGreSQL ORM (Object Relational Mapping) Information Schema PG Catalog Interleaved Tables Vertica Spark Change Data Capture

The intro and outro music is from The Hug by The Freak Fandan

Summary

Using a multi-model database in your applications can greatly reduce the amount of infrastructure and complexity required. ArangoDB is a storage engine that supports documents, dey/value, and graph data formats, as well as being fast and scalable. In this episode Jan Steeman and Jan Stücke explain where Arango fits in the crowded database market, how it works under the hood, and how you can start working with it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Jan Stücke and Jan Steeman about ArangoDB, a multi-model distributed database for graph, document, and key/value storage.

Interview

Introduction How did you get involved in the area of data management? Can you give a high level description of what ArangoDB is and the motivation for creating it?

What is the story behind the name?

How is ArangoDB constructed?

How does the underlying engine store the data to allow for the different ways of viewing it?

What are some of the benefits of multi-model data storage?

When does it become problematic?

For users who are accustomed to a relational engine, how do they need to adjust their approach to data modeling when working with Arango? How does it compare to OrientDB? What are the options for scaling a running system?

What are the limitations in terms of network architecture or data volumes?

One of the unique aspects of ArangoDB is the Foxx framework for embedding microservices in the data layer. What benefits does that provide over a three tier architecture?

What mechanisms do you have in place to prevent data breaches from security vulnerabilities in the Foxx code? What are some of the most interesting or surprising uses of this functionality that you have seen?

What are some of the most challenging technical and business aspects of building and promoting ArangoDB? What do you have planned for the future of ArangoDB?

Contact Info

Jan Steemann

jsteemann on GitHub @steemann on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

ArangoDB Köln Multi-model Database Graph Algorithms Apache 2 C++ ArangoDB Foxx Raft Protocol Target Partners RocksDB AQL (ArangoDB Query Language) OrientDB PostGreSQL OrientDB Studio Google Spanner 3-Tier Architecture Thomson-Reuters Arango Search Dell EMC Google S2 Index ArangoDB Geographic Functionality JSON Schema

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Summary

Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Yair Weinberger about Alooma, a company providing data pipelines as a service

Interview

Introduction How did you get involved in the area of data management? What is Alooma and what is the origin story? How is the Alooma platform architected?

I want to go into stream VS batch here What are the most challenging components to scale?

How do you manage the underlying infrastructure to support your SLA of 5 nines? What are some of the complexities introduced by processing data from multiple customers with various compliance requirements?

How do you sandbox user’s processing code to avoid security exploits?

What are some of the potential pitfalls for automatic schema management in the target database? Given the large number of integrations, how do you maintain the

What are some challenges when creating integrations, isn’t it simply conforming with an external API?

For someone getting started with Alooma what does the workflow look like? What are some of the most challenging aspects of building and maintaining Alooma? What are your plans for the future of Alooma?

Contact Info

LinkedIn @yairwein on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Alooma Convert Media Data Integration ESB (Enterprise Service Bus) Tibco Mulesoft ETL (Extract, Transform, Load) Informatica Microsoft SSIS OLAP Cube S3 Azure Cloud Storage Snowflake DB Redshift BigQuery Salesforce Hubspot Zendesk Spark The Log: What every software engineer should know about real-time data’s unifying abstraction by Jay Kreps RDBMS (Relational Database Management System) SaaS (Software as a Service) Change Data Capture Kafka Storm Google Cloud PubSub Amazon Kinesis Alooma Code Engine Zookeeper Idempotence Kafka Streams Kubernetes SOC2 Jython Docker Python Javascript Ruby Scala PII (Personally Identifiable Information) GDPR (General Data Protection Regulation) Amazon EMR (Elastic Map Reduce) Sequoia Capital Lightspeed Investors Redis Aerospike Cassandra MongoDB

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kamil Bajda-Pawlikowski about Presto and his experiences with supporting it at Starburst Data

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Presto is?

What are some of the common use cases and deployment patterns for Presto?

How does Presto compare to Drill or Impala? What is it about Presto that led you to building a business around it? What are some of the most challenging aspects of running and scaling Presto? For someone who is using the Presto SQL interface, what are some of the considerations that they should keep in mind to avoid writing poorly performing queries?

How does Presto represent data for translating between its SQL dialect and the API of the data stores that it interfaces with?

What are some cases in which Presto is not the right solution? What types of support have you found to be the most commonly requested? What are some of the types of tooling or improvements that you have made to Presto in your distribution?

What are some of the notable changes that your team has contributed upstream to Presto?

Contact Info

Website E-mail Twitter – @starburstdata Twitter – @prestodb

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Starburst Data Presto Hadapt Hadoop Hive Teradata PrestoCare Cost Based Optimizer ANSI SQL Spill To Disk Tempto Benchto Geospatial Functions Cassandra Accumulo Kafka Redis PostGreSQL

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Summary

The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Andy Eschbacher of Carto. He dscribes some of the complexities inherent to working with geospatial data, how they are handling it, and some of the interesting use cases that they enable for their customers. Next is Todd Blaschka, COO of TigerGraph. He explains how graph databases differ from relational engines, where graph algorithms are useful, and how TigerGraph is built to alow for fast and scalable operation.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and last week I attended the Open Data Science Conference in Boston and recorded a few brief interviews on-site. In this second part you will hear from Andy Eschbacher of Carto about the challenges of managing geospatial data, as well as Todd Blaschka of TigerGraph about graph databases and how his company has managed to build a fast and scalable platform for graph storage and traversal.

Interview

Andy Eschbacher From Carto

What are the challenges associated with storing geospatial data? What are some of the common misconceptions that people have about working with geospatial data?

Contact Info

andy-esch on GitHub @MrEPhysics on Twitter Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Carto Geospatial Analysis GeoJSON

Todd Blaschka From TigerGraph

What are graph databases and how do they differ from relational engines? What are some of the common difficulties that people have when deling with graph algorithms? How does data modeling for graph databases differ from relational stores?

Contact Info

LinkedIn @toddblaschka on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

TigerGraph Graph Databases

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Alan Anders, the CTO of Applecart about their challenges with getting Spark to scale for constructing an entity graph from multiple data sources. Next I spoke with Stepan Pushkarev, the CEO, CTO, and Co-Founder of Hydrosphere.io about the challenges of running machine learning models in production and how his team tracks key metrics and samples production data to re-train and re-deploy those models for better accuracy and more robust operation.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and this week I attended the Open Data Science Conference in Boston and recorded a few brief interviews on-site. First up you’ll hear from Alan Anders, the CTO of Applecart about their challenges with getting Spark to scale for constructing an entity graph from multiple data sources. Next I spoke with Stepan Pushkarev, the CEO, CTO, and Co-Founder of Hydrosphere.io about the challenges of running machine learning models in production and how his team tracks key metrics and samples production data to re-train and re-deploy those models for better accuracy and more robust operation.

Interview

Alan Anders from Applecart

What are the challenges of gathering and processing data from multiple data sources and representing them in a unified manner for merging into single entities? What are the biggest technical hurdles at Applecart?

Contact Info

@alanjanders on Twitter LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Spark DataBricks DataBricks Delta Applecart

Stepan Pushkarev from Hydrosphere.io

What is Hydropshere.io? What metrics do you track to determine when a machine learning model is not producing an appropriate output? How do you determine which data points to sample for retraining the model? How does the role of a machine learning engineer differ from data engineers and data scientists?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Hydrosphere Machine Learning Engineer

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Business Intelligence software is often cumbersome and requires specialized knowledge of the tools and data to be able to ask and answer questions about the state of the organization. Metabase is a tool built with the goal of making the act of discovering information and asking questions of an organizations data easy and self-service for non-technical users. In this episode the CEO of Metabase, Sameer Al-Sakran, discusses how and why the project got started, the ways that it can be used to build and share useful reports, some of the useful features planned for future releases, and how to get it set up to start using it in your environment.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Sameer Al-Sakran about Metabase, a free and open source tool for self service business intelligence

Interview

Introduction How did you get involved in the area of data management? The current goal for most companies is to be “data driven”. How would you define that concept?

How does Metabase assist in that endeavor?

What is the ratio of users that take advantage of the GUI query builder as opposed to writing raw SQL?

What level of complexity is possible with the query builder?

What have you found to be the typical use cases for Metabase in the context of an organization? How do you manage scaling for large or complex queries? What was the motivation for using Clojure as the language for implementing Metabase? What is involved in adding support for a new data source? What are the differentiating features of Metabase that would lead someone to choose it for their organization? What have been the most challenging aspects of building and growing Metabase, both from a technical and business perspective? What do you have planned for the future of Metabase?

Contact Info

Sameer

salsakran on GitHub @sameer_alsakran on Twitter LinkedIn

Metabase

Website @metabase on Twitter metabase on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Expa Metabase Blackjet Hadoop Imeem Maslow’s Hierarchy of Data Needs 2 Sided Marketplace Honeycomb Interview Excel Tableau Go-JEK Clojure React Python Scala JVM Redash How To Lie With Data Stripe Braintree Payments

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

The information about how data is acquired and processed is often as important as the data itself. For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. These systems are frequently cumbersome and difficult to maintain, so Octopai was founded to alleviate that burden. In this episode Amnon Drori, CEO and co-founder of Octopai, discusses the business problems he witnessed that led him to starting the company, how their systems are able to provide valuable tools and insights, and the direction that their product will be taking in the future.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Amnon Drori about OctopAI and the benefits of metadata management

Interview

Introduction How did you get involved in the area of data management? What is OctopAI and what was your motivation for founding it? What are some of the types of information that you classify and collect as metadata? Can you talk through the architecture of your platform? What are some of the challenges that are typically faced by metadata management systems? What is involved in deploying your metadata collection agents? Once the metadata has been collected what are some of the ways in which it can be used? What mechanisms do you use to ensure that customer data is segregated?

How do you identify and handle sensitive information during the collection step?

What are some of the most challenging aspects of your technical and business platforms that you have faced? What are some of the plans that you have for OctopAI going forward?

Contact Info

Amnon

LinkedIn @octopai_amnon on Twitter

OctopAI

@OctopaiBI on Twitter Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

OctopAI Metadata Metadata Management Data Integrity CRM (Customer Relationship Management) ERP (Enterprise Resource Planning) Business Intelligence ETL (Extract, Transform, Load) Informatica SAP Data Governance SSIS (SQL Server Integration Services) Vertica Airflow Luigi Oozie GDPR (General Data Privacy Regulation) Root Cause Analysis

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

The rate of change in the data engineering industry is alternately exciting and exhausting. Joe Crobak found his way into the work of data management by accident as so many of us do. After being engrossed with researching the details of distributed systems and big data management for his work he began sharing his findings with friends. This led to his creation of the Hadoop Weekly newsletter, which he recently rebranded as the Data Engineering Weekly newsletter. In this episode he discusses his experiences working as a data engineer in industry and at the USDS, his motivations and methods for creating a newsleteter, and the insights that he has gleaned from it.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Joe Crobak about his work maintaining the Data Engineering Weekly newsletter, and the challenges of keeping up with the data engineering industry.

Interview

Introduction How did you get involved in the area of data management? What are some of the projects that you have been involved in that were most personally fulfilling?

As an engineer at the USDS working on the healthcare.gov and medicare systems, what were some of the approaches that you used to manage sensitive data? Healthcare.gov has a storied history, how did the systems for processing and managing the data get architected to handle the amount of load that it was subjected to?

What was your motivation for starting a newsletter about the Hadoop space?

Can you speak to your reasoning for the recent rebranding of the newsletter?

How much of the content that you surface in your newsletter is found during your day-to-day work, versus explicitly searching for it? After over 5 years of following the trends in data analytics and data infrastructure what are some of the most interesting or surprising developments?

What have you found to be the fundamental skills or areas of experience that have maintained relevance as new technologies in data engineering have emerged?

What is your workflow for finding and curating the content that goes into your newsletter? What is your personal algorithm for filtering which articles, tools, or commentary gets added to the final newsletter? How has your experience managing the newsletter influenced your areas of focus in your work and vice-versa? What are your plans going forward?

Contact Info

Data Eng Weekly Email Twitter – @joecrobak Twitter – @dataengweekly

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

USDS National Labs Cray Amazon EMR (Elastic Map-Reduce) Recommendation Engine Netflix Prize Hadoop Cloudera Puppet healthcare.gov Medicare Quality Payment Program HIPAA NIST National Institute of Standards and Technology PII (Personally Identifiable Information) Threat Modeling Apache JBoss Apache Web Server MarkLogic JMS (Java Message Service) Load Balancer COBOL Hadoop Weekly Data Engineering Weekly Foursquare NiFi Kubernetes Spark Flink Stream Processing DataStax RSS The Flavors of Data Science and Engineering CQRS Change Data Capture Jay Kreps

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Managing an analytics project can be difficult due to the number of systems involved and the need to ensure that new information can be delivered quickly and reliably. That challenge can be met by adopting practices and principles from lean manufacturing and agile software development, and the cross-functional collaboration, feedback loops, and focus on automation in the DevOps movement. In this episode Christopher Bergh discusses ways that you can start adding reliability and speed to your workflow to deliver results with confidence and consistency.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Christopher Bergh about DataKitchen and the rise of DataOps

Interview

Introduction How did you get involved in the area of data management? How do you define DataOps?

How does it compare to the practices encouraged by the DevOps movement? How does it relate to or influence the role of a data engineer?

How does a DataOps oriented workflow differ from other existing approaches for building data platforms? One of the aspects of DataOps that you call out is the practice of providing multiple environments to provide a platform for testing the various aspects of the analytics workflow in a non-production context. What are some of the techniques that are available for managing data in appropriate volumes across those deployments? The practice of testing logic as code is fairly well understood and has a large set of existing tools. What have you found to be some of the most effective methods for testing data as it flows through a system? One of the practices of DevOps is to create feedback loops that can be used to ensure that business needs are being met. What are the metrics that you track in your platform to define the value that is being created and how the various steps in the workflow are proceeding toward that goal?

In order to keep feedback loops fast it is necessary for tests to run quickly. How do you balance the need for larger quantities of data to be used for verifying scalability/performance against optimizing for cost and speed in non-production environments?

How does the DataKitchen platform simplify the process of operationalizing a data analytics workflow? As the need for rapid iteration and deployment of systems to capture, store, process, and analyze data becomes more prevalent how do you foresee that feeding back into the ways that the landscape of data tools are designed and developed?

Contact Info

LinkedIn @ChrisBergh on Twitter Email

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

DataOps Manifesto DataKitchen 2017: The Year Of DataOps Air Traffic Control Chief Data Officer (CDO) Gartner W. Edwards Deming DevOps Total Quality Management (TQM) Informatica Talend Agile Development Cattle Not Pets IDE (Integrated Devel

Summary

Cloud computing and ubiquitous virtualization have changed the ways that our applications are built and deployed. This new environment requires a new way of tracking and addressing the security of our systems. ThreatStack is a platform that collects all of the data that your servers generate and monitors for unexpected anomalies in behavior that would indicate a breach and notifies you in near-realtime. In this episode ThreatStack’s director of operations, Pete Cheslock, and senior infrastructure security engineer, Patrick Cable, discuss the data infrastructure that supports their platform, how they capture and process the data from client systems, and how that information can be used to keep your systems safe from attackers.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Pete Cheslock and Pat Cable about the data infrastructure and security controls at ThreatStack

Interview

Introduction How did you get involved in the area of data management? Why don’t you start by explaining what ThreatStack does?

What was lacking in the existing options (services and self-hosted/open source) that ThreatStack solves for?

Can you describe the type(s) of data that you collect and how it is structured? What is the high level data infrastructure that you use for ingesting, storing, and analyzing your customer data?

How do you ensure a consistent format of the information that you receive? How do you ensure that the various pieces of your platform are deployed using the proper configurations and operating as intended? How much configuration do you provide to the end user in terms of the captured data, such as sampling rate or additional context?

I understand that your original architecture used RabbitMQ as your ingest mechanism, which you then migrated to Kafka. What was your initial motivation for that change?

How much of a benefit has that been in terms of overall complexity and cost (both time and infrastructure)?

How do you ensure the security and provenance of the data that you collect as it traverses your infrastructure? What are some of the most common vulnerabilities that you detect in your client’s infrastructure? For someone who wants to start using ThreatStack, what does the setup process look like? What have you found to be the most challenging aspects of building and managing the data processes in your environment? What are some of the projects that you have planned to improve the capacity or capabilities of your infrastructure?

Contact Info

Pete Cheslock

@petecheslock on Twitter Website petecheslock on GitHub

Patrick Cable

@patcable on Twitter Website patcable on GitHub

ThreatStack

Website @threatstack on Twitter threatstack on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

ThreatStack SecDevO