Summary In this episode of the Data Engineering Podcast Matt Topper, president of UberEther, talks about the complex challenge of identity, credentials, and access control in modern data platforms. With the shift to composable ecosystems, integration burdens have exploded, fracturing governance and auditability across warehouses, lakes, files, vector stores, and streaming systems. Matt shares practical solutions, including propagating user identity via JWTs, externalizing policy with engines like OPA/Rego and Cedar, and using database proxies for native row/column security. He also explores catalog-driven governance, lineage-based label propagation, and OpenTDF for binding policies to data objects. The conversation covers machine-to-machine access, short-lived credentials, workload identity, and constraining access by interface choke points, as well as lessons from Zanzibar-style policy models and the human side of enforcement. Matt emphasizes the need for trust composition - unifying provenance, policy, and identity context - to answer questions about data access, usage, and intent across the entire data path.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Matt Topper about the challenges of managing identity and access controls in the context of data systemsInterview IntroductionHow did you get involved in the area of data management?The data ecosystem is a uniquely challenging space for creating and enforcing technical controls for identity and access control. What are the key considerations for designing a strategy for addressing those challenges?For data acess the off-the-shelf options are typically on either extreme of too coarse or too granular in their capabilities. What do you see as the major factors that contribute to that situation?Data governance policies are often used as the primary means of identifying what data can be accesssed by whom, but translating that into enforceable constraints is often left as a secondary exercise. How can we as an industry make that a more manageable and sustainable practice?How can the audit trails that are generated by data systems be used to inform the technical controls for identity and access?How can the foundational technologies of our data platforms be improved to make identity and authz a more composable primitive?How does the introduction of streaming/real-time data ingest and delivery complicate the challenges of security controls?What are the most interesting, innovative, or unexpected ways that you have seen data teams address ICAM?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ICAM?What are the aspects of ICAM in data systems that you are paying close attention to?What are your predictions for the industry adoption or enforcement of those controls?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links UberEtherJWT == JSON Web TokenOPA == Open Policy AgentRegoPingIdentityOktaMicrosoft EntraSAML == Security Assertion Markup LanguageOAuthOIDC == OpenID ConnectIDP == Identity ProviderKubernetesIstioAmazon CEDAR policy languageAWS IAMPII == Personally Identifiable InformationCISO == Chief Information Security OfficerOpenTDFOpenFGAGoogle ZanzibarRisk Management FrameworkModel Context ProtocolGoogle Data ProjectTPM == Trusted Platform ModulePKI == Public Key InfrastructurePassskeysDuckLakePodcast EpisodeAccumuloJDBCOpenBaoHashicorp VaultLDAPThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary

A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! If you’re a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex’s magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It’s like having an analytics co-pilot built right into where you’re already doing your work. Then, when you’re ready to share, you can use Hex’s drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products

Interview

Introduction How did you get involved in the area of data management? Can you describe what the term "linked data product" means and some examples of when you might build one?

What is the overlap between knowledge graphs and "linked data products"?

What is JSON-LD?

What are the domains in which it is typically used? How does it assist in developing linked data products?

what are the characterist

Summary Unstructured data takes many forms in an organization. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc. Another category of unstructured data that every business deals with is PDFs, Word documents, workstation backups, and countless other types of information. Aparavi was created to tame the sprawl of information across machines, datacenters, and clouds so that you can reduce the amount of duplicate data and save time and money on managing your data assets. In this episode Rod Christensen shares the story behind Aparavi and how you can use it to cut costs and gain value for the long tail of your unstructured data.

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 their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing Rod Christensen about Aparavi, a platform designed to find and unlock the value of data, no matter where it lives

Interview

Introduction How did you get involved in the area of data management? Can you describe what Aparavi is and the story behind it? Who are the target customers for Aparavi and how does that inform your product roadmap and messaging? What are some of th

Summary Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short. The team at Airbyte is adding a new entry to that list with the goal of making robust and easy to use data integration more accessible to teams who want or need to maintain full control of their data. In this episode co-founders John Lafleur and Michel Tricot share the story of how and why they created Airbyte, discuss the project’s design and architecture, and explain their vision of what an open soure data integration platform should offer. If you are struggling to maintain your extract and load pipelines or spending time on integrating with a new system when you would prefer to be working on other projects then this is definitely a conversation worth listening to.

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 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Michel Tricot and John Lafleur about Airbyte, an open source framework for building data integration pipelines.

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Airbyte is and the story behind it? Businesses and data engineers have a variety of options for how to manage their data integration. How would you characterize the overall landscape and how does Airbyte distinguish itself in that space? How would you characterize your target users?

How have those personas instructed the priorities and design of Airbyte? What do you see as the benefits and tradeoffs of a UI oriented data integration platform as compared to a code first approach?

what are the complex/challenging elements of data integration that makes it such a slippery problem? motivation for creating open source ELT as a business Can you describe how the Airbyte platform is implemented?

What was your motivation for choosing Java as the primary language?

incidental complexity of forcing all connectors to be packaged as containers shortcomings of the Singer specification/motivation for creating a backwards incompatible interface perceived potential for community adoption of Airbyte specification tradeoffs of using JSON as interchange format vs. e.g. protobuf/gRPC/Avro/etc.

information lost when converting records to JSON types/how to preserve that information (e.g. field constraints, valid enums, etc.)

interfaces/extension points for integrating with other tools, e.g. Dagster abstraction layers for simplifying implementation of new connectors tradeoffs of storing all connectors in a monorepo with the Airbyte core

impact of community adoption/contributions

What is involved in setting up an Airbyte installation? What are the available axes for scaling an Airbyte deployment? challenges of setting up and maintaining CI environment for Airbyte How are you managing governance and long term sustainability of the project? What are some of the most interesting, unexpected, or innovative ways that you have seen Airbyte used? What are the most interesting, unexpected, or challenging lessons that you have learned while building Airbyte? When is Airbyte the wrong choice? What do you have planned for the future of the project?

Contact Info

Michel

LinkedIn @MichelTricot on Twitter michel-tricot on GitHub

John

LinkedIn @JeanLafleur on Twitter johnlafleur on GitHub

Parting Question

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

Links

Airbyte Liveramp Fivetran

Podcast Episode

Stitch Data Matillion DataCoral

Podcast Episode

Singer Meltano

Podcast Episode

Airflow

Podcast.init Episode

Kotlin Docker Monorepo Airbyte Specification Great Expectations

Podcast Episode

Dagster

Data Engineering Podcast Episode Podcast.init Episode

Prefect

Podcast Episode

DBT

Podcast Episode

Kubernetes Snowflake

Podcast Episode

Redshift Presto Spark Parquet

Podcast Episode

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

Support Data Engineering Podcast

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

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. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. 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 This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?

Cleanliness/accuracy

What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides?

How has that architecture evolved from when you first started? What would you do differently if you were to start over today?

Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Alex

@alexcrdean on Twitter LinkedIn

Snowplow

@snowplowdata on Twitter

Parting Question

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

Links

Snowplow

GitHub

Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics

Podcast Interview

Redshift SnowflakeDB Snowplow Insights Googl

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

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

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

Summary

To process your data you need to know what shape it has, which is why schemas are important. When you are processing that data in multiple systems it can be difficult to ensure that they all have an accurate representation of that schema, which is why Confluent has built a schema registry that plugs into Kafka. In this episode Ewen Cheslack-Postava explains what the schema registry is, how it can be used, and how they built it. He also discusses how it can be extended for other deployment targets and use cases, and additional features that are planned for future releases.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Ewen Cheslack-Postava about the Confluent Schema Registry

Interview

Introduction How did you get involved in the area of data engineering? What is the schema registry and what was the motivating factor for building it? If you are using Avro, what benefits does the schema registry provide over and above the capabilities of Avro’s built in schemas? How did you settle on Avro as the format to support and what would be involved in expanding that support to other serialization options? Conversely, what would be involved in using a storage backend other than Kafka? What are some of the alternative technologies available for people who aren’t using Kafka in their infrastructure? What are some of the biggest challenges that you faced while designing and building the schema registry? What is the tipping point in terms of system scale or complexity when it makes sense to invest in a shared schema registry and what are the alternatives for smaller organizations? What are some of the features or enhancements that you have in mind for future work?

Contact Info

ewencp on GitHub Website @ewencp on Twitter

Parting Question

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

Links

Kafka Confluent Schema Registry Second Life Eve Online Yes, Virginia, You Really Do Need a Schema Registry JSON-Schema Parquet Avro Thrift Protocol Buffers Zookeeper Kafka Connect

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

Summary

Yelp needs to be able to consume and process all of the user interactions that happen in their platform in as close to real-time as possible. To achieve that goal they embarked on a journey to refactor their monolithic architecture to be more modular and modern, and then they open sourced it! In this episode Justin Cunningham joins me to discuss the decisions they made and the lessons they learned in the process, including what worked, what didn’t, and what he would do differently if he was starting over today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Justin Cunningham about Yelp’s data pipeline

Interview with Justin Cunningham

Introduction How did you get involved in the area of data engineering? Can you start by giving an overview of your pipeline and the type of workload that you are optimizing for? What are some of the dead ends that you experienced while designing and implementing your pipeline? As you were picking the components for your pipeline, how did you prioritize the build vs buy decisions and what are the pieces that you ended up building in-house? What are some of the failure modes that you have experienced in the various parts of your pipeline and how have you engineered around them? What are you using to automate deployment and maintenance of your various components and how do you monitor them for availability and accuracy? While you were re-architecting your monolithic application into a service oriented architecture and defining the flows of data, how were you able to make the switch while verifying that you were not introducing unintended mutations into the data being produced? Did you plan to open-source the work that you were doing from the start, or was that decision made after the project was completed? What were some of the challenges associated with making sure that it was properly structured to be amenable to making it public? What advice would you give to anyone who is starting a brand new project and how would that advice differ for someone who is trying to retrofit a data management architecture onto an existing project?

Keep in touch

Yelp Engineering Blog Email

Links

Kafka Redshift ETL Business Intelligence Change Data Capture LinkedIn Data Bus Apache Storm Apache Flink Confluent Apache Avro Game Days Chaos Monkey Simian Army PaaSta Apache Mesos Marathon SignalFX Sensu Thrift Protocol Buffers JSON Schema Debezium Kafka Connect Apache Beam

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