Data Engineering Podcast

Summary Data Engineering is a broad and constantly evolving topic, which makes it difficult to teach in a concise and effective manner. Despite that, Daniel Molnar and Peter Fabian started the Pipeline Academy to do exactly that. In this episode they reflect on the lessons that they learned while teaching the first cohort of their bootcamp how to be effective data engineers. By focusing on the fundamentals, and making everyone write code, they were able to build confidence and impart the importance of context for their students. 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! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Daniel Molnar and Peter Fabian about the lessons that they learned from their first cohort at the Pipeline data engineering academy Interview Introduction How did you get involved in the area of data management? Can you start by sharing the curriculum and learning goals for the students? How did you set a common baseline for all of the students to build from throughout the program? What was your process for determining the structure of the tasks and the tooling used? What were some of the topics/tools that the students had the most difficulty with? What topics/tools were the easiest to grasp? What are some difficulties that you encountered while trying to teach different concepts? How did you deal with the tension of teaching the fundamentals while tying them to toolchains that hiring managers are looking for? What are the successes that you had with this cohort and what changes are you making to your approach/curriculum to build on them? What are some of the failures that you encountered and what lessons have you taken from them? How did the pandemic impact your overall plan and execution of the initial cohort? What were the skills that you focused on for interview preparation? What level of ongoing support/engagement do you have with students once they complete the curriculum? What are the most interesting, innovative, or unexpected solutions that you saw from your students? What are the most interesting, unexpected, or challenging lessons that you have learned while working with your first cohort? When is a bootcamp the wrong approach for skill development? What do you have planned for the future of the Pipeline Academy? Contact Info Daniel LinkedIn Website @soobrosa on Twitter Peter LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Pipeline Academy Blog Scikit Pandas Urchin Kafka Three "C"s – Context, Confidence, and Code Prefect Podcast Episode Great Expectations Podcast Episode Podcast.__init__ Episode Docker Kubernetes Become a Data Engineer On A Shoestring James Mickens The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The database is the core of any system because it holds the data that drives your entire experience. We spend countless hours designing the data model, updating engine versions, and tuning performance. But how confident are you that you have configured it to be as performant as possible, given the dozens of parameters and how they interact with each other? Andy Pavlo researches autonomous database systems, and out of that research he created OtterTune to find the optimal set of parameters to use for your specific workload. In this episode he explains how the system works, the challenge of scaling it to work across different database engines, and his hopes for the future of database systems. 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! 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. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Andy Pavlo about OtterTune, a system to continuously monitor and improve database performance via machine learning Interview Introduction How did you get involved in the area of data management? Can you describe what OtterTune is and the story behind it? How does it relate to your work with NoisePage? What are the challenges that database administrators, operators, and users run into when working with, configuring, and tuning transactional systems? What are some of the contributing factors to the sprawling complexity of the configurable parameters for these databases? Can you describe how OtterTune is implemented? What are some of the aggregate benefits that OtterTune can gain by running as a centralized service and learning from all of the systems that it connects to? What are some of the assumptions that you made when starting the commercialization of this technology that have been challenged or invalidated as you began working with initial customers? How have the design and goals of the system changed or evolved since you first began working on it? What is involved in adding support for a new database engine? How applicable are the OtterTune capabilities to analytical database engines? How do you handle tuning for variable or evolving workloads? What are some of the most interesting or esoteric configuration options that you have come across while working on OtterTune? What are some that made you facepalm? What are the most interesting, innovative, or unexpected ways that you have seen OtterTune used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on OtterTune? When is OtterTune the wrong choice? What do you have planned for the future of OtterTune? Contact Info CMU Page apavlo on GitHub @andy_pavlo on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links OtterTune CMU (Carnegie Mellon University) Brown University Michael Stonebraker H-Store Learned Indexes NoisePage Oracle DB PostgreSQL Podcast Episode MySQL RDS Gaussian Process Model Reinforcement Learning AWS Aurora MVCC (Multi-Version Concurrency Control) Puppet VectorWise GreenPlum Snowflake Podcast Episode PGTune MySQL Tuner SIGMOD The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Working with unstructured data has typically been a motivation for a data lake. The challenge is imposing enough order on the platform to make it useful. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable. In this episode he shares the goals of the Unstruk Data Warehouse, how it is architected to extract asset metadata and build a searchable knowledge graph from the information, and the myriad ways that the system can be used. If you are wondering how to deal with all of the information that doesn’t fit in your databases or data warehouses, then this episode is for you. 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! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Kirk Marple about Unstruk Data, a company that is building a data warehouse for unstructured data that ofers automated data preparation via metadata enrichment, integrated compute, and graph-based search Interview Introduction How did you get involved in the area of data management? Can you describe what Unstruk Data is and the story behind it? What would you classify as "unstructured data"? What are some examples of industries that rely on large or varied sets of unstructured data? What are the challenges for analytics that are posed by the different categories of unstructured data? What is the current state of the industry for working with unstructured data? What are the unique capabilities that Unstruk provides and how does it integrate with the rest of the ecosystem? Where does it sit in the overall landscape of data tools? Can you describe how the Unstruk data warehouse is implemented? What are the assumptions that you had at the start of this project that have been challenged as you started working through the technical implementation and customer trials? How has the design and architecture evolved or changed since you began working on it? How do you handle versioning of data, given the potential for individual files to be quite large? What are some of the considerations that users should have in mind when modeling their data in the warehouse? Can you talk through the workflow of ingesting and analyzing data with Unstruk? How do you manage data enrichment/integration with structured data sources? What are the most interesting, innovative, or unexpected ways that you have seen the technology of Unstruk used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with the Unstruk platform? When is Unstruk the wrong choice? What do you have planned for the future of Unstruk? Contact Info LinkedIn @KirkMarple on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Unstruk Data TIFF ROSBag HDF5 Media/Digital Asset Management Data Mesh SAN NAS Knowledge Graph Entity Extraction OCR (Optical Character Recognition) Cloud Native Cosmos DB Azure Functions Azure EventHub Azure Cognitive Search GraphQL KNative Schema.org Pinecone Vector Database Podcast Episode Dublin Core Metadata Initiative Knowledge Management The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary When you build a machine learning model, the first step is always to load your data. Typically this means downloading files from object storage, or querying a database. To speed up the process, why not build the model inside the database so that you don’t have to move the information? In this episode Paige Roberts explains the benefits of pushing the machine learning processing into the database layer and the approach that Vertica has taken for their implementation. If you are looking for a way to speed up your experimentation, or an easy way to apply AutoML then this conversation is for you. 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! 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. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Paige Roberts about machine learning workflows inside the database Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the current state of the market for databases that support in-process machine learning? What are the motivating factors for running a machine learning workflow inside the database? What styles of ML are feasible to do inside the database? (e.g. bayesian inference, deep learning, etc.) What are the performance implications of running a model training pipeline within the database runtime? (both in terms of training performance boosts, and database performance impacts) Can you describe the architecture of how the machine learning process is managed by the database engine? How do you manage interacting with Python/R/Jupyter/etc. when working within the database? What is the impact on data pipeline and MLOps architectures when using the database to manage the machine learning workflow? What are the most interesting, innovative, or unexpected ways that you have seen in-database ML used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on machine learning inside the database? When is in-database ML the wrong choice? What are the recent trends/changes in machine learning for the database that you are excited for? Contact Info LinkedIn Blog @RobertsPaige on Twitter @PaigeEwing on Twitter Parting 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. 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 hosts@dataengineeringpodcast.com) with your story. 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 Links Vertica SyncSort Hortonworks Infoworld – 8 databases supporting in-database machine learning Power BI Podcast Episode Grafana Tableau K-Means Clustering MPP == Massively Parallel Processing AutoML Random Forest PMML == Predictive Model Markup Language SVM == Support Vector Machine Naive Bayes XGBoost Pytorch Tensorflow Neural Magic Tensorflow Frozen Graph Parquet ORC Avro CNCF == Cloud Native Computing Foundation Hotel California VerticaPy Pandas Podcast.__init__ Episode Jupyter Notebook UDX Unifying Analytics Presentation Hadoop Yarn Holden Karau Spark Vertica Academy The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. Now they offer the technologies that they run internally to external users of their cloud platform. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. He shares some of the common patterns for building pipelines to power business intelligence dashboards, machine learning applications, and data warehouses. If you’ve ever been overwhelmed or confused by the array of services available in the Google Cloud Platform then this episode is for you. 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! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Lak Lakshmanan about the suite of services for data and analytics in Google Cloud Platform. Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the tools and products that are offered as part of Google Cloud for data and analytics? How do the various systems relate to each other for building a full workflow? How do you balance the need for clean integration between services with the need to make them useful in isolation when used as a single component of a data platform? What have you found to be the primary motivators for customers who are adopting GCP for some or all of their data workloads? What are some of the challenges that new users of GCP encounter when working with the data and analytics products that it offers? What are the systems that you have found to be easiest to work with? Which are the most challenging to work with, whether due to the kinds of problems that they are solving for, or due to their user experience design? How has your work with customers fed back into the products that you are building on top of? What are some examples of architectural or software patterns that are unique to the GCP product suite? What are the most interesting, innovative, or unexpected ways that you have seen Google Cloud’s data and analytics services used? What are the most interesting, unexpected, or challenging lessons that you have learned while working at Google and helping customers succeed in their data and analytics efforts? What are some of the new capabilities, new services, or industry trends that you are most excited for? Contact Info LinkedIn @lak_gcp on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Google Cloud Data and Analytics Services Forrester Wave Dremel BigQuery MapReduce Cloud Spanner Spanner Paper Hadoop Tensorflow Google Cloud SQL Apache Spark Dataproc Dataflow Apache Beam Databricks Mixpanel Avalanche data warehouse Kubernetes GKE (Google Kubernetes Engine) Google Cloud Run Android Youtube Google Translate Teradata Power BI Podcast Episode AI Platform Notebooks GitHub Data Repository Stack Overflow Questions Data Repository PyPI Download Statistics Recommendations AI Pub/Sub Bigtable Datastream Change Data Capture Podcast Episode About Debezium for CDC Podcast Episode About CDC with Datacoral Document AI Google Meet Data Governance Podcast Episodes The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The way to build maintainable software and systems is through composition of individual pieces. By making those pieces high quality and flexible they can be used in surprising ways that the original creators couldn’t have imagined. One such component that has gone above and beyond its originally envisioned use case is BookKeeper, a distributed storage system that is optimized for durability and speed. In this episode Matteo Merli shares the story behind the creation of BookKeeper, the various ways that it is being used today, and the architectural aspects that make it such a strong building block for projects such as Pulsar. He also shares some of the other interesting systems that have been built on top of it and an amusing war story of running it at scale in its early years. 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! 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. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Matteo Merli about Apache BookKeeper, a scalable, fault-tolerant, and low-latency storage service optimized for real-time workloads Interview Introduction How did you get involved in the area of data management? Can you describe what BookKeeper is and the story behind it? What are the most notable features/capabilities of BookKeeper? What are some of the ways that BookKeeper is being used? How has your work on Pulsar influenced the features and product direction of BookKeeper? Can you describe the architecture of a BookKeeper cluster? How have the design and goals of BookKeeper changed or evolved over time? What is the impact of record-oriented storage on data distribution/allocation within the cluster when working with variable record sizes? What are some of the operational considerations that users should be aware of? What are some of the most interesting/compelling features from your perspective? What are some of the most often overlooked or misunderstood capabilities of BookKeeper? What are the most interesting, innovative, or unexpected ways that you have seen BookKeeper used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on BookKeeper? When is BookKeeper the wrong choice? What do you have planned for the future of BookKeeper? Contact Info LinkedIn @merlimat on Twitter merlimat on GitHub Parting 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. 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 hosts@dataengineeringpodcast.com) with your story. 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 Links Apache BookKeeper Apache Pulsar Podcast Episode StreamNative Podcast Episode Hadoop NameNode Apache Zookeeper Podcast Episode ActiveMQ Write Ahead Log (WAL) BookKeeper Architecture RocksDB LSM == Log-Structured Merge-Tree RAID Controller Pravega Podcast Episode BookKeeper etcd Metadata Storage LevelDB Ceph Podcast Episode Direct IO Page Cache The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary SQL is the most widely used language for working with data, and yet the tools available for writing and collaborating on it are still clunky and inefficient. Frustrated with the lack of a modern IDE and collaborative workflow for managing the SQL queries and analysis of their big data environments, the team at Pinterest created Querybook. In this episode Justin Mejorada-Pier and Charlie Gu share the story of how the initial prototype for a data catalog ended up as one of their most widely used interfaces to their analytical data. They also discuss the unique combination of features that it offers, how it is implemented, and the path to releasing it as open source. Querybook is an impressive and unique piece of technology that is well worth exploring, so listen and try it out today. 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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Justin Mejorada-Pier and Charlie Gu about Querybook, an open source IDE for your big data projects Interview Introduction How did you get involved in the area of data management? Can you describe what Querybook is and the story behind it? What are the main use cases or workflows that Querybook is designed for? What are the shortcomings of dashboarding/BI tools that make something like Querybook necessary? The tag line calls out the fact that Querybook is an IDE for "big data". What are the manifestations of that focus in the feature set and user experience? Who are the target users of Querybook and how does that inform the feature priorities and user experience? Can you describe how Querybook is architected? How have the goals and design changed or evolved since you first began working on it? What were some of the assumptions or design choices that you had to unwind in the process of open sourcing it? What is the workflow for someone building a DataDoc with Querybook? What is the experience of working as a collaborator on an analysis? How do you handle lifecycle management of query results? What are your thoughts on the potential for extending Querybook beyond SQL-oriented analysis and integrating something like Jupyter kernels? What are the most interesting, innovative, or unexpected ways that you have seen Querybook used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Querybook? When is Querybook the wrong choice? What do you have planned for the future of Querybook? Contact Info Justin LinkedIn Website Charlie czgu on GitHub Parting 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. 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 hosts@dataengineeringpodcast.com) with your story. 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 Links Querybook Announcing Querybook as Open Source Pinterest University of Waterloo Superset Podcast Episode Podcast.__init__ Episode Sequel Pro Presto Trino Podcast Episode Flask uWSGI Podcast.__init__ Episode Celery Redis SocketIO Elasticsearch Podcast Episode Amundsen Podcast Episode Apache Atlas DataHub Podcast Episode Okta LDAP (Lightweight Directory Access Protocol) Grand Rounds The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. In order for the true value of your data to be realized without burning out your engineers you need a way for everyone to get access to the information they care about. To help make that a more tractable problem Blake Burch co-founded Shipyard. In this episode he explains the utility of a low code solution that lets non engineers create their own self-serve pipelines, how the Shipyard platform is designed to make that possible, and how it allows engineers to create reusable tasks to satisfy the specific needs of the business. This is an interesting conversation about how to make data more accessible and more useful by improving the user experience of the tools that we create. 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! 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. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Blake Burch about Shipyard, and his mission to create the easiest way for data teams to launch, monitor, and share resilient pipelines with less engineering Interview Introduction How did you get involved in the area of data management? Can you describe what you are building at Shipyard and the story behind it? What are the main goals that you have for Shipyard? How does it compare to other data orchestration frameworks in the market? Who are the target users of Shipyard and how does that influence the features and design of the product? What are your thoughts on the role of data orchestration in the business? How is the Shipyard platform implemented? What was your process for identifying the core requirements of the platform? How have the design and goals of the system evolved since you first began working on it? Can you describe the workflow of building a data workflow with Shipyard? How do you manage the dependency chain across tasks in the execution graph? (e.g. task-based, data assets, etc.) How do you handle testing and data quality management in your workflows? What is the interface for creating custom task definitions? How do you address dependencies and sandboxing for custom code? What is your approach to developing templates? What are the operational challenges that you have had to address to manage scaling and multi-tenancy in your platform? What are the most interesting, innovative, or unexpected ways that you have seen Shipyard used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Shipyard? When is Shipyard the wrong choice? What do you have planned for the future of Shipyard? Contact Info LinkedIn @BlakeBurch_ on Twitter Website blakeburch on GitHub Parting 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. 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 hosts@dataengineeringpodcast.com) with your story. 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 Links Shipyard Zapier Airtable BigQuery Snowflake Podcast Episode Docker ECS == Elastic Container Service Great Expectations Podcast Episode Monte Carlo Podcast Episode Soda Data Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary The data warehouse has become the focal point of the modern data platform. With increased usage of data across businesses, and a diversity of locations and environments where data needs to be managed, the warehouse engine needs to be fast and easy to manage. Yellowbrick is a data warehouse platform that was built from the ground up for speed, and can work across clouds and all the way to the edge. In this episode CTO Mark Cusack explains how the engine is architected, the benefits that speed and predictable pricing has for the organization, and how you can simplify your platform by putting the warehouse close to the data, instead of the other way around. 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! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Mark Cusack about Yellowbrick, a data warehouse designed for distributed clouds Interview Introduction How did you get involved in the area of data management? Can you start by describing what Yellowbrick is and some of the story behind it? What does the term "distributed cloud" signify and what challenges are associated with it? How would you characterize Yellowbrick’s position in the database/DWH market? How is Yellowbrick architected? How have the goals and design of the platform changed or evolved over time? How does Yellowbrick maintain visibility across the different data locations that it is responsible for? What capabilities does it offer for being able to join across the disparate "clouds"? What are some data modeling strategies that users should consider when designing their deployment of Yellowbrick? What are some of the capabilities of Yellowbrick that you find most useful or technically interesting? For someone who is adopting Yellowbrick, what is the process for getting it integrated into their data systems? What are the most underutilized, overlooked, or misunderstood features of Yellowbrick? What are the most interesting, innovative, or unexpected ways that you have seen Yellowbrick used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Yellowbrick? When is Yellowbrick the wrong choice? What do you have planned for the future of the product? Contact Info LinkedIn @markcusack on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Yellowbrick Teradata Rainstor Distributed Cloud Hybrid Cloud SwimOS Podcast Episode Kafka Pulsar Podcast Episode Snowflake Podcast Episode AWS Redshift MPP == Massively Parallel Processing Presto Trino Podcast Episode L3 Cache NVMe Reactive Programming Coroutine Star Schema Denodo Lexis Nexis Vertica Netezza Grenplum PostgreSQL Podcast Episode Clickhouse Podcast Episode Erasure Coding The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary Machine learning models use vectors as the natural mechanism for representing their internal state. The problem is that in order for the models to integrate with external systems their internal state has to be translated into a lower dimension. To eliminate this impedance mismatch Edo Liberty founded Pinecone to build database that works natively with vectors. In this episode he explains how this technology will allow teams to accelerate the speed of innovation, how vectors make it possible to build more advanced search functionality, and how Pinecone is architected. This is an interesting conversation about how reconsidering the architecture of your systems can unlock impressive new capabilities. 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! 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. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Edo Liberty about Pinecone, a vector database for powering machine learning and similarity search Interview Introduction How did you get involved in the area of data management? Can you start by describing what Pinecone is and the story behind it? What are some of the contexts where someone would want to perform a similarity search? What are the considerations that someone should be aware of when deciding between Pinecone and Solr/Lucene for a search oriented use case? What are some of the other use cases that Pinecone enables? In the absence of Pinecone, what kinds of systems and solutions are people building to address those use cases? Where does Pinecone sit in the lifecycle of data and how does it integrate with the broader data management ecosystem? What are some of the systems, tools, or frameworks that Pinecone might replace? How is Pinecone implemented? How has the architecture evolved since you first began working on it? What are the most complex or difficult aspects of building Pinecone? Who is your target user and how does that inform the user experience design and product development priorities? For someone who wants to start using Pinecone, what is involved in populating it with data building an analysis or service with it? What are some of the data modeling considerations when building a set of vectors in Pinecone? What are some of the most interesting, unexpected, or innovative ways that you have seen Pinecone used? What are the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Pinecone technology and business? When is Pinecone the wrong choice? What do you have planned for the future of Pinecone? Contact Info Website LinkedIn Parting 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. 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 hosts@dataengineeringpodcast.com) with your story. 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 Links Pinecone Theoretical Physics High Dimensional Geometry AWS Sagemaker Visual Cortex Temporal Lobe Inverted Index Elasticsearch Podcast Episode Solr Lucene NMSLib Johnson-Lindenstrauss Lemma The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast