Data Engineering Podcast

By Tobias MaceyTechnology Education

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Typical duration
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per episode

Based on Podcast App listening data

Data Engineering Podcast episodes

May 18, 2021Unlocking The Power of Data Lineage In Your Platform with OpenLineage
Summary
Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end. The complicating factor is that every framework, platform, and product has its own concepts of how to store, represent, and expose that information. In order to eliminate the wasted effort of building custom integrations every time you want to combine lineage information across systems Julien Le Dem introduced the OpenLineage specification. In this episode he explains his motivations for starting the effort, the far-reaching benefits that it can provide to the industry, and how you can start integrating it into your data platform today. This is an excellent conversation about how competing companies can still find mutual benefit in co-operating on open standards.
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 Julien Le Dem about Open Lineage, a new standard for structuring metadata to enable interoperability across the ecosystem of data management tools.
Interview
Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of what the Open Lineage project is and the story behind it?
What is the current state of the ecosystem for generating and sharing metadata between systems?
What are your goals for the OpenLineage effort?
What are the biggest conceptual or consistency challenges that you are facing in defining a metadata model that is broad and flexible enough to be widely used while still being prescriptive enough to be useful?
What is the current state of the project? (e.g. code available, maturity of the specification, etc.)
What are some of the ideas or assumptions that you had at the beginning of this project that have had to be revisited as you iterate on the definition and implementation?
What are some of the projects/organizations/etc. that have committed to supporting or adopting OpenLineage?
What problem domain(s) are best suited to adopting OpenLineage?
What are some of the problems or use cases that you are explicitly not including in scope for OpenLineage?
For someone who already has a lineage and/or metadata catalog, what is involved in evolving that system to work well with OpenLineage?
What are some of the downstream/long-term impacts that you anticipate or hope that this standardization effort will generate?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on the OpenLineage effort?
What do you have planned for the future of the project?
Contact Info
LinkedIn
@J_ on Twitter
julienledem 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 [email protected]) 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
OpenLineage
Marquez
Podcast Episode
Hadoop
Pig
Apache Parquet
Podcast Episode
Doug Cutting
Avro
Apache Arrow
Service Oriented Architecture
Data Lineage
Apache Atlas
DataHub
Podcast Episode
Amundsen
Podcast Episode
Egeria
Pandas
Podcast.__init__ Episode
Apache Spark
EXIF
JSON Schema
OpenTelemetry
Podcast.__init__ Episode
OpenTracing
Superset
Podcast.__init__ Episode
Data Engineering Podcast Episode
Iceberg
Podcast Episode
Great Expectations
Podcast Episode
dbt
Podcast Episode
Data Mesh
Podcast Episode
The map is not the territory
Kafka
Apache Flink
Apache Storm
Kafka Streams
Stone Soup
Apache Beam
Linux Foundation AI & Data
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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58 min
May 14, 2021Building Your Data Warehouse On Top Of PostgreSQL
Summary
There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. If you want to build a warehouse that gives you both control and flexibility then you might consider building on top of the venerable PostgreSQL project. In this episode Thomas Richter and Joshua Drake share their advice on how to build a production ready data warehouse with Postgres.
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 Thomas Richter and Joshua Drake about using Postgres as your data warehouse
Interview
Introduction
How did you get involved in the area of data management?
Can you start by establishing a working definition of what constitutes a data warehouse for the purpose of this discussion?
What are the limitations for out-of-the-box Postgres when trying to use it for these workloads?
There are a large and growing number of options for data warehouse style workloads. How would you categorize the different systems and what is PostgreSQL’s position in that ecosystem?
What do you see as the motivating factors for a team or organization to select from among those categories?
Why would someone want to use Postgres as their data warehouse platform rather than using a purpose-built engine?
What is the cost/performance equation for Postgres as compared to other data warehouse solutions?
For someone who wants to turn Postgres into a data warehouse engine, what are their options?
What are the relative tradeoffs of the different open source and commercial offerings? (e.g. Citus, cstore_fdw, zedstore, Swarm64, Greenplum, etc.)
One of the biggest areas of growth right now is in the "cloud data warehouse" market where storage and compute are decoupled. What are the options for making that possible with Postgres? (e.g. using foreign data wrappers for interacting with data lake storage (S3, HDFS, Alluxio, etc.))
What areas of work are happening in the Postgres community for upcoming releases to make it more easily suited to data warehouse/analytical workloads?
What are some of the most interesting, innovative, or unexpected ways that you have seen Postgres used in analytical contexts?
What are the most interesting, unexpected, or challenging lessons that you have learned from your own experiences of building analytical systems with Postgres?
When is Postgres the wrong choice for a data warehouse?
What are you most excited for/what are you keeping an eye on in upcoming releases of Postgres and its ecosystem?
Contact Info
Thomas
LinkedIn
JD
LinkedIn
@linuxhiker on Twitter
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
PostgreSQL
Podcast Episode
Swarm64
Podcast Episode
Command Prompt Inc.
IBM
Cognos
OLAP Cube
MariaDB
MySQL
Powell’s Books
DBase
Practical PostgreSQL
Netezza
Presto
Trino
Apache Drill
Parquet
Parquet Foreign Data Wrapper
Snowflake
Podcast Episode
Amazon RDS
Amazon Aurora
Hyperscale
Citus
TimescaleDB
Podcast Episode
Followup Podcast Episode
Greenplum
zedstore
Redshift
Microsoft SQL Server
Postgres Tablespaces
Debezium
Podcast Episode
EDI == Enterprise Data Integration
Change Data Capture
Podcast Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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1 hr 16 min
May 11, 2021Making Analytical APIs Fast With Tinybird
Summary
Building an API for real-time data is a challenging project. Making it robust, scalable, and fast is a full time job. The team at Tinybird wants to make it easy to turn a continuous stream of data into a production ready API or data product. In this episode CEO Jorge Sancha explains how they have architected their system to handle high data throughput and fast response times, and why they have invested heavily in Clickhouse as the core of their platform. This is a great conversation about the challenges of building a maintainable business from a technical and product perspective.
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.
Ascend.io — recognized as a 2021 Gartner Cool Vendor in Enterprise AI Operationalization and Engineering—empowers data teams to to build, scale, and operate declarative data pipelines with 95% less code and zero maintenance. Connect to any data source using Ascend’s new flex code data connectors, rapidly iterate on transformations and send data to any destination in a fraction of the time it traditionally takes—just ask companies like Harry’s, HNI, and Mayvenn. Sound exciting? Come join the team! We’re hiring data engineers, so head on over to dataengineeringpodcast.com/ascend and check out our careers page to learn more.
Your host is Tobias Macey and today I’m interviewing Jorge Sancha about Tinybird, a platform to easily build analytical APIs for real-time data
Interview
Introduction
How did you get involved in the area of data management?
Can you start by describing what you are building at Tinybird and the story behind it?
What are some of the types of use cases that your customers are focused on?
What are the areas of complexity that come up when building analytical APIs that are often overlooked when first designing a system to operate on and expose real-time data?
What are the supporting systems that are necessary and useful for operating this kind of system which contribute to the overall time and engineering cost beyond the baseline functionality?
How is the Tinybird platform architected?
How have the goals and implementation of Tinybird changed or evolved since you first began building it?
What was your criteria for selecting the core building block of your platform, and how did that lead to your choice to build on top of Clickhouse?
What are some of the sharp edges that you have run into while operating Clickhouse?
What are some of the custom tools or systems that you have built to help deal with them?
What are some of the performance challenges that an API built with Tinybird might run into?
What are the considerations that users should be aware of to avoid introducing performance issues?
How do you handle multi-tenancy in your platform? (e.g. separate clusters, in-database quotas, etc.)
For users of Tinybird, can you talk through the workflow of getting it integrated into their platform and designing an API from their data?
What are some of the most interesting, innovative, or unexpected ways that you have seen Tinybird used?
What are the most interesting, unexpected, or challenging lessons that you have learned while building and growing Tinybird?
When is Tinybird the wrong choice?
What do you have planned for the future of the product and business?
Contact Info
@jorgesancha on Twitter
LinkedIn
jorgesancha on GitHub
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
Tinybird
Carto
PostgreSQL
Podcast Episode
PostGIS
Clickhouse
Podcast Episode
Kafka
Tornado
Podcast.__init__ Episode
Redis
Formula 1
Web Application Firewall
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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55 min
May 07, 2021Making Spark Cloud Native At Data Mechanics
Summary
Spark is one of the most well-known frameworks for data processing, whether for batch or streaming, ETL or ML, and at any scale. Because of its popularity it has been deployed on every kind of platform you can think of. In this episode Jean-Yves Stephan shares the work that he is doing at Data Mechanics to make it sing on Kubernetes. He explains how operating in a cloud-native context simplifies some aspects of running the system while complicating others, how it simplifies the development and experimentation cycle, and how you can get a head start using their pre-built Spark container. This is a great conversation for understanding how new ways of operating systems can have broader impacts on how they are being used.
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 Jean-Yves Stephan about Data Mechanics, a cloud-native Spark platform for data engineers
Interview
Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of what you are building at Data Mechanics and the story behind it?
What are the operational characteristics of Spark that make it difficult to run in a cloud-optimized environment?
How do you handle retries, state redistribution, etc. when instances get pre-empted during the middle of a job execution?
What are some of the tactics that you have found useful when designing jobs to make them more resilient to interruptions?
What are the customizations that you have had to make to Spark itself?
What are some of the supporting tools that you have built to allow for running Spark in a Kubernetes environment?
How is the Data Mechanics platform implemented?
How have the goals and design of the platform changed or evolved since you first began working on it?
How does running Spark in a container/Kubernetes environment change the ways that you and your customers think about how and where to use it?
How does it impact the development workflow for data engineers and data scientists?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while building the Data Mechanics product?
When is Spark/Data Mechanics the wrong choice?
What do you have planned for the future of the platform?
Contact Info
LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
Data Mechanics
Databricks
Stanford
Andrew Ng
Mining Massive Datasets
Spark
Kubernetes
Spot Instances
Infiniband
Data Mechanics Spark Container Image
Delight – Spark monitoring utility
Terraform
Blue/Green Deployment
Spark Operator for Kubernetes
JupyterHub
Jupyter Enterprise Gateway
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
41 min
May 04, 2021The Grand Vision And Present Reality of DataOps
Summary
The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, discuss the grand vision and present realities of DataOps. They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. If you are wondering how to get control of your data platforms and bring all of your stakeholders onto the same page 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!
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 Max Beauchemin, Lior Gavish, and Kevin Stumpf about the real world challenges of embracing DataOps practices and systems, and how to keep things secure as you scale
Interview
Introduction
How did you get involved in the area of data management?
Before we get started, can you each give your definition of what "DataOps" means to you?
How does this differ from "business as usual" in the data industry?
What are some of the things that DataOps isn’t (despite what marketers might say)?
What are the biggest difficulties that you have faced in going from concept to production with a workflow or system intended to power self-serve access to other members of the organization?
What are the weak points in the current state of the industry, whether technological or social, that contribute to your greatest sense of unease from a security perspective?
As founders of companies that aim to facilitate adoption of various aspects of DataOps, how are you applying the products that you are building to your own internal systems?
How does security factor into the design of robust DataOps systems? What are some of the biggest challenges related to security when it comes to putting these systems into production?
What are the biggest differences between DevOps and DataOps, particularly when it concerns designing distributed systems?
What areas of the DataOps landscape do you think are ripe for innovation?
Nowadays, it seems like new DataOps companies are cropping up every day to try and solve some of these problems. Why do you think DataOps is becoming such an important component of the modern data stack?
There’s been a lot of conversation recently around the "rise of the data engineer" versus other roles in the data ecosystem (i.e. data scientist or data analyst). Why do you think that is?
What are some of the most valuable lessons that you have learned from working with your customers about how to apply DataOps principles?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while building your respective platforms and businesses?
What are the industry trends that you are each keeping an eye on to inform you future product direction?
Contact Info
Kevin
LinkedIn
kevinstumpf on GitHub
@kevinstumpf on Twitter
Maxime
LinkedIn
@mistercrunch on Twitter
mistercrunch on GitHub
Lior
LinkedIn
@lgavish 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 [email protected]) 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
Tecton
Monte Carlo
Superset
Preset
Barracuda Networks
Feature Store
DataOps
DevOps
Data Catalog
Amundsen
OpenLineage
The Downfall of the Data Engineer
Hashicorp Vault
Reverse ELT
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
58 min
April 27, 2021Self Service Data Exploration And Dashboarding With Superset
Summary
The reason for collecting, cleaning, and organizing data is to make it usable by the organization. One of the most common and widely used methods of access is through a business intelligence dashboard. Superset is an open source option that has been gaining popularity due to its flexibility and extensible feature set. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to data and deliver analytics. He digs into how it integrates with your data stack, how you can extend it to fit your use case, and why open source systems are a good choice for your business intelligence. If you haven’t already tried out Superset then this conversation is well worth your time. Give it a listen and then take it for a test drive 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!
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 Max Beauchemin about Superset, an open source platform for data exploration, dashboards, and business intelligence
Interview
Introduction
How did you get involved in the area of data management?
Can you start by describing what Superset is?
Superset is becoming part of the reference architecture for a modern data stack. What are the factors that have contributed to its popularity over other tools such as Redash, Metabase, Looker, etc.?
Where do dashboarding and exploration tools like Superset fit in the responsibilities and workflow of a data engineer?
What are some of the challenges that Superset faces in being performant when working with large data sources?
Which data sources have you found to be the most challenging to work with?
What are some anti-patterns that users of Superset might run into when building out a dashboard?
What are some of the ways that users can surface data quality indicators (e.g. freshness, lineage, check results, etc.) in a Superset dashboard?
Another trend in analytics and dashboard tools is providing actionable insights. How can Superset support those use cases where a business user or analyst wants to perform an action based on the data that they are being shown?
How can Superset factor into a data governance strategy for the business?
What are some of the most interesting, innovative, or unexpected ways that you have seen Superset used?
dogfooding
What are the most interesting, unexpected, or challenging lessons that you have learned from working on Superset and founding Preset?
When is Superset the wrong choice?
What do you have planned for the future of Superset and Preset?
Contact Info
LinkedIn
@mistercrunch on Twitter
mistercrunch 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 [email protected]) 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
Superset
Podcast.__init__ Episode
Preset
ASP (Active Server Pages)
VBScript
Data Warehouse Institute
Ralph Kimball
Bill Inmon
Ubisoft
Hadoop
Tableau
Looker
Podcast Episode
The Future of Business Intelligence Is Open Source
Supercharging Apache Superset
Redash
Podcast.__init__ Episode
Metabase
Podcast Episode
The Rise Of The Data Engineer
AirBnB Data University
Python DBAPI
SQLAlchemy
Druid
SQL Common Table Expressions
SQL Window Functions
Data Warehouse Semantic Layer
Amundsen
Podcast Episode
Open Lineage
Datakin
Marquez
Podcast Episode
Apache Arrow
Podcast.__init__ Episode with Wes McKinney
Apache Parquet
DataHub
Podcast Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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48 min
April 20, 2021Moving Machine Learning Into The Data Pipeline at Cherre
Summary
Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Sometimes, however, one of those transformations is actually a full-fledged machine learning project in its own right. In this episode Tal Galfsky explains how he and the team at Cherre tackled the problem of messy data for Addresses by building a natural language processing and entity resolution system that is served as an API to the rest of their pipelines. He discusses the myriad ways that addresses are incomplete, poorly formed, and just plain wrong, why it was a big enough pain point to invest in building an industrial strength solution for it, and how it actually works under the hood. After listening to this you’ll look at your data pipelines in a new light and start to wonder how you can bring more advanced strategies into the cleaning and transformation process.
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 Tal Galfsky about how Cherre is bringing order to the messy problem of physical addresses and entity resolution in their data pipelines.
Interview
Introduction
How did you get involved in the area of data management?
Started as physicist and evolved into Data Science
Can you start by giving a brief recap of what Cherre is and the types of data that you deal with?
Cherre is a company that connects data
We’re not a data vendor, in that we don’t sell data, primarily
We help companies connect and make sense of their data
The real estate market is historically closed, gut let, behind on tech
What are the biggest challenges that you deal with in your role when working with real estate data?
Lack of a standard domain model in real estate.
Ontology. What is a property? Each data source, thinks about properties in a very different way. Therefore, yielding similar, but completely different data.
QUALITY (Even if the dataset are talking about the same thing, there are different levels of accuracy, freshness).
HIREARCHY. When is one source better than another
What are the teams and systems that rely on address information?
Any company that needs to clean or organize (make sense) their data, need to identify, people, companies, and properties.
Our clients use Address resolution in multiple ways. Via the UI or via an API. Our service is both external and internal so what I build has to be good enough for the demanding needs of our data science team, robust enough for our engineers, and simple enough that non-expert clients can use it.
Can you give an example for the problems involved in entity resolution
Known entity example.
Empire state buidling.
To resolve addresses in a way that makes sense for the client you need to capture the real world entities. Lots, buildings, units.
Identify the type of the object (lot, building, unit)
Tag the object with all the relevant addresses
Relations to other objects (lot, building, unit)
What are some examples of the kinds of edge cases or messiness that you encounter in addresses?
First class is string problems.
Second class component problems.
third class is geocoding.
I understand that you have developed a service for normalizing addresses and performing entity resolution to provide canonical references for downstream analyses. Can you give an overview of what is involved?
What is the need for the service. The main requirement here is connecting an address to lot, building, unit with latitude and longitude coordinates
How were you satisfying this requirement previously?
Before we built our model and dedicated service we had a basic prototype for pipeline only to handle NYC addresses.
What were the motivations for designing and implementing this as a service?
Need to expand nationwide and to deal with client queries in real time.
What are some of the other data sources that you rely on to be able to perform this normalization and resolution?
Lot data, building data, unit data, Footprints and address points datasets.
What challenges do you face in managing these other sources of information?
Accuracy, hirearchy, standardization, unified solution, persistant ids and primary keys
Digging into the specifics of your solution, can you talk through the full lifecycle of a request to resolve an address and the various manipulations that are performed on it?
String cleaning, Parse and tokenize, standardize, Match
What are some of the other pieces of information in your system that you would like to see addressed in a similar fashion?
Our named entity solution with connection to knowledge graph and owner unmasking.
What are some of the most interesting, unexpected, or challenging lessons that you learned while building this address resolution system?
Scaling nyc geocode example. The NYC model was exploding a subset of the options for messing up an address. Flexibility. Dependencies. Client exposure.
Now that you have this system running in production, if you were to start over today what would you do differently?
a lot but at this point the module boundaries and client interface are defined in such way that we are able to make changes or completely replace any given part of it without breaking anything client facing
What are some of the other projects that you are excited to work on going forward?
Named entity resolution and Knowledge Graph
Contact Info
LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
BigQuery is huge asset and in particular UDFs but they don’t support API calls or python script
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 [email protected]) 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
Cherre
Podcast Episode
Photonics
Knowledge Graph
Entity Resolution
BigQuery
NLP == Natural Language Processing
dbt
Podcast Episode
Airflow
Podcast.__init__ Episode
Datadog
Podcast Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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49 min
April 13, 2021Exploring The Expanding Landscape Of Data Professions with Josh Benamram of Databand
Summary
"Business as usual" is changing, with more companies investing in data as a first class concern. As a result, the data team is growing and introducing more specialized roles. In this episode Josh Benamram, CEO and co-founder of Databand, describes the motivations for these emerging roles, how these positions affect the team dynamics, and the types of visibility that they need into the data platform to do their jobs effectively. He also talks about how his experience working with these teams informs his work at Databand. If you are wondering how to apply your talents and interests to working with data then this episode is a must 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 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 Josh Benamram about the continued evolution of roles and responsibilities in data teams and their varied requirements for visibility into the data stack
Interview
Introduction
How did you get involved in the area of data management?
Can you start by discussing the set of roles that you see in a majority of data teams?
What new roles do you see emerging, and what are the motivating factors?
Which of the more established positions are fracturing or merging to create these new responsibilities?
What are the contexts in which you are seeing these role definitions used? (e.g. small teams, large orgs, etc.)
How do the increased granularity/specialization of responsibilities across data teams change the ways that data and platform architects need to think about technology investment?
What are the organizational impacts of these new types of data work?
How do these shifts in role definition change the ways that the individuals in the position interact with the data platform?
What are the types of questions that practitioners in different roles are asking of the data that they are working with? (e.g. what is the lineage of this asset vs. what is the distribution of values in this column, etc.)
How can metrics and observability data about pipelines and data systems help to support these various roles?
What are the different ways of measuring data quality for the needs of these roles?
How is the work you are doing at Databand informed by these changing needs?
One of the big challenges caused by data systems is the varying modes of access and interaction across the different stakeholders and activities. How can data platform teams and vendors help to surface useful metrics and information across these various interfaces without forcing users into a new or unfamiliar workflow?
What are some of the long-term impacts that you foresee in the data ecosystem and ways of interacting with data as a result of the current trend toward more specialized tasks?
As a vendor working to provide useful context to these practitioners what are some of the most interesting, unexpected, or challenging lessons that you have learned?
What do you have planned for the future of Databand?
Contact Info
Email
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 [email protected]) 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
Databand
Website
Platform
Open Core
More data engineering stories & best practices
Atlassian
Chartio
Data Mesh Article
Podcast Episode
Grafana
Metabase
Superset
Podcast.__init__ Episode
Snowflake
Podcast Episode
Spark
Airflow
Podcast.__init__ Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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1 hr 9 min
April 06, 2021Put Your Whole Data Team On The Same Page With Atlan
Summary
One of the biggest obstacles to success in delivering data products is cross-team collaboration. Part of the problem is the difference in the information that each role requires to do their job and where they expect to find it. This introduces a barrier to communication that is difficult to overcome, particularly in teams that have not reached a significant level of maturity in their data journey. In this episode Prukalpa Sankar shares her experiences across multiple attempts at building a system that brings everyone onto the same page, ultimately bringing her to found Atlan. She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page.
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 Prukalpa Sankar about Atlan, a modern data workspace that makes collaboration among data stakeholders easier, increasing efficiency and agility in data projects
Interview
Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of what you are building at Atlan and some of the story behind it?
Who are the target users of Atlan?
What portions of the data workflow is Atlan responsible for?
What components of the data stack might Atlan replace?
How would you characterize Atlan’s position in the current data ecosystem?
What makes Atlan stand out from other systems for data cataloguing, metadata management, or data governance?
What types of data assets (e.g. structured vs unstructured, textual vs binary, etc.) is Atlan designed to understand?
Can you talk through how Atlan is implemented?
How have the goals and design of the platform changed or evolved since you first began working on it?
What are some of the early assumptions that you have had to revisit or reconsider?
What is involved in getting Atlan deployed and integrated into an existing data platform?
Beyond the technical aspects, what are the business processes that teams need to implement to be successful when incorporating Atlan into their systems?
Once Atlan is set up, what is a typical workflow for an individual and their team to collaborate on a set of data assets, or building out a new processing pipeline?
What are some useful steps for introducing all of the stakeholders to the system and workflow?
What are the available extension points for managing data in systems that aren’t supported by Atlan out of the box?
What are some of the most interesting, innovative, or unexpected ways that you have seen Atlan used?
What are the most interesting, unexpected, or challenging lessons that you have learned while building Atlan?
When is Atlan the wrong choice?
What do you have planned for the future of the product?
Contact Info
LinkedIn
@prukalpa 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 [email protected]) 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
Atlan
India’s National Data Platform
World Economic Forum
UN
Gates Foundation
GitHub
Figma
Snowflake
Redshift
Databricks
DBT
Sisense
Looker
Apache Atlas
Immuta
DataHub
Datakin
Aapache Ranger
Great Expectations
Trino
Airflow
Dagster
Privacera
Databand
Cloudformation
Grafana
Deequ
We Failed to Set Up a Data Catalog 3x. Here’s Why.
Analysing the analysers book
OpenAPI
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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58 min
March 30, 2021Data Quality Management For The Whole Team With Soda Data
Summary
Data quality is on the top of everyone’s mind recently, but getting it right is as challenging as ever. One of the contributing factors is the number of people who are involved in the process and the potential impact on the business if something goes wrong. In this episode Maarten Masschelein and Tom Baeyens share the work they are doing at Soda to bring everyone on board to make your data clean and reliable. They explain how they started down the path of building a solution for managing data quality, their philosophy of how to empower data engineers with well engineered open source tools that integrate with the rest of the platform, and how to bring all of the stakeholders onto the same page to make your data great. There are many aspects of data quality management and it’s always a treat to learn from people who are dedicating their time and energy to solving it for everyone.
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 Maarten Masschelein and Tom Baeyens about the work are doing at Soda to power data quality management
Interview
Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of what you are building at Soda?
What problem are you trying to solve?
And how are you solving that problem?
What motivated you to start a business focused on data monitoring and data quality?
The data monitoring and broader data quality space is a segment of the industry that is seeing a huge increase in attention recently. Can you share your perspective on the current state of the ecosystem and how your approach compares to other tools and products?
who have you created Soda for (e.g platform engineers, data engineers, data product owners etc) and what is a typical workflow for each of them?
How do you go about integrating Soda into your data infrastructure?
How has the Soda platform been architected?
Why is this architecture important?
How have the goals and design of the system changed or evolved as you worked with early customers and iterated toward your current state?
What are some of the challenges associated with the ongoing monitoring and testing of data?
what are some of the tools or techniques for data testing used in conjunction with Soda?
What are some of the most interesting, innovative, or unexpected ways that you have seen Soda being used?
What are the most interesting, unexpected, or challenging lessons that you have learned while building the technology and business for Soda?
When is Soda the wrong choice?
What do you have planned for the future?
Contact Info
Maarten
LinkedIn
@masscheleinm on Twitter
Tom
LinkedIn
@tombaeyens 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 [email protected]) 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
Soda Data
Soda SQL
RedHat
Collibra
Spark
Getting Things Done by David Allen (affiliate link)
Slack
OpsGenie
DBT
Airflow
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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58 min

About Data Engineering Podcast

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This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some…