Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their data lake platform. In this episode Upsolver CEO Ori Rafael and CTO Yoni Iny describe how they have grown their platform deliberately to allow for layering SQL on top of a robust foundation for creating and operating a data lake, how to bring more people on board to work with the data being collected, and the unique benefits that a data lake provides. This was an interesting look at the impact that the interface to your data can have on who is empowered to work with it.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ori Rafael and Yoni Iny about building a data lake for the DBA at Upsolver

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing your definition of what a data lake is and what it is comprised of? We talked last in November of 2018. How has the landscape of data lake technologies and adoption changed in that time?

How has Upsolver changed or evolved since we last spoke?

How has the evolution of the underlying technologies impacted your implementation and overall product strategy?

What are some of the common challenges that accompany a data lake implementation? How do those challenges influence the adoption or viability of a data lake? How does the introduction of a universal SQL layer change the staffing requirements for building and maintaining a data lake?

What are the advantages of a data lake over a data warehouse if everything is being managed via SQL anyway?

What are some of the underlying realities of the data systems that power the lake which will eventually need to be understood by the operators of the platform? How is the SQL layer in Upsolver implemented?

What are the most challenging or complex aspects of managing the underlying technologies to provide automated partitioning, indexing, etc.?

What are the main concepts that you need to educate your customers on? What are some of the pitfalls that users should be aware of? What features of your platform are often overlooked or underutilized which you think should be more widely adopted? What have you found to be the most interesting, unexpected, or challenging lessons learned while building the technical and business elements of Upsolver? What do you have planned for the future?

Contact Info

Ori

LinkedIn

Yoni

yoniiny on GitHub LinkedIn

Parting Question

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

Links

Upsolver

Podcast Episode

DBA == Database Administrator IDF == Israel Defense Forces Data Lake Eventual Consistency Apache Spark Redshift Spectrum Azure Synapse Analytics SnowflakeDB

Podcast Episode

BigQuery Presto

Podcast Episode

Apache Kafka Cartesian Product kSQLDB

Podcast Episode

Eventador

Podcast Episode

Materialize

Podcast Episode

Common Table Expressions Lambda Architecture Kappa Architecture Apache Flink

Podcast Episode

Reinforcement Learning Cloudformation GDPR

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

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Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it?

How does it compare to the other available platforms for data warehousing? How does it differ from traditional data warehouses?

How does the performance and flexibility affect the data modeling requirements?

Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces? Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity?

What are some of the current limitations that you are struggling with?

For someone getting started with Snowflake what is involved with loading data into the platform?

What is their workflow for allocating and scaling compute capacity and running anlyses?

One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen? What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about? When is SnowflakeDB the wrong choice? What are some of the plans for the future of SnowflakeDB?

Contact Info

LinkedIn Website @KentGraziano on Twitter

Parting Question

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

Links

SnowflakeDB

Free Trial Stack Overflow

Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto

Podcast Episode

SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal Form Data Vault Modeling Dimensional Modeling JSON AVRO Parquet SnowflakeDB Virtual Warehouses CRM == Customer Relationship Management Master Data Management

Podcast Episode

FoundationDB

Podcast Episode

Apache Spark

Podcast Episode

SSIS == SQL Server Integration Services Talend Informatica Fivetran

Podcast Episode

Matillion Apache Kafka Snowpipe Snowflake Data Exchange OLTP == Online Transaction Processing GeoJSON Snowflake Documentation SnowAlert Splunk Data Catalog

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

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Summary In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DBT is and your motivation for creating it? Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline? Can you talk through the workflow for someone using DBT? One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented? The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented?

Are these packages driven by Fishtown Analytics or the dbt community?

What are the limitations of modeling everything as a SELECT statement? Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings?

What are your thoughts on higher level approaches to SQL that compile down to the specific statements?

Can you explain how DBT is implemented and how the design has evolved since you first began working on it? What are some of the features of DBT that are often overlooked which you find particularly useful? What are some of the most interesting/unexpected/innovative ways that you have seen DBT used? What are the additional features that the commercial version of DBT provides? What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT? When is it the wrong choice? What do you have planned for the future of DBT?

Contact Info

Email @drebanin on Twitter drebanin on GitHub

Parting Question

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

Links

DBT Fishtown Analytics 8Tracks Internet Radio Redshift Magento Stitch Data Fivetran Airflow Business Intelligence Jinja template language BigQuery Snowflake Version Control Git Continuous Integration Test Driven Development Snowplow Analytics

Podcast Episode

dbt-utils We Can Do Better Than SQL blog post from EdgeDB EdgeDB Looker LookML

Podcast Interview

Presto DB

Podcast Interview

Spark SQL Hive Azure SQL Data Warehouse Data Warehouse Data Lake Data Council Conference Slowly Changing Dimensions dbt Archival Mode Analytics Periscope BI dbt docs dbt repository

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

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Summary

Business intelligence is a necessity for any organization that wants to be able to make informed decisions based on the data that they collect. Unfortunately, it is common for different portions of the business to build their reports with different assumptions, leading to conflicting views and poor choices. Looker is a modern tool for building and sharing reports that makes it easy to get everyone on the same page. In this episode Daniel Mintz explains how the product is architected, the features that make it easy for any business user to access and explore their reports, and how you can use it for your organization 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 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 Daniel Mintz about Looker, a a modern data platform that can serve the data needs of an entire company

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Looker is and the problem that it is aiming to solve?

How do you define business intelligence?

How is Looker unique from other approaches to business intelligence in the enterprise?

How does it compare to open source platforms for BI?

Can you describe the technical infrastructure that supports Looker? Given that you are connecting to the customer’s data store, how do you ensure sufficient security? For someone who is using Looker, what does their workflow look like?

How does that change for different user roles (e.g. data engineer vs sales management)

What are the scaling factors for Looker, both in terms of volume of data for reporting from, and for user concurrency? What are the most challenging aspects of building a business intelligence tool and company in the modern data ecosystem?

What are the portions of the Looker architecture that you would do differently if you were to start over today?

What are some of the most interesting or unusual uses of Looker that you have seen? What is in store for the future of Looker?

Contact Info

LinkedIn

Parting Question

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

Links

Looker Upworthy MoveOn.org LookML SQL Business Intelligence Data Warehouse Linux Hadoop BigQuery Snowflake Redshift DB2 PostGres ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Airflow Luigi NiFi Data Curation Episode Presto Hive Athena DRY (Don’t Repeat Yourself) Looker Action Hub Salesforce Marketo Twilio Netscape Navigator Dynamic Pricing Survival Analysis DevOps BigQuery ML Snowflake Data Sharehouse

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

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