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PODCAST

Data Engineering Podcast

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 of the topics that you will find here.

technology

All Episodes

54:43

Fast And Flexible Headless Data Analytics With Cube.JS

Data Engineering Podcast ·

2021/12/21

Summary One of the perennial challenges of data analytics is having a consistent set of definitions, along with a flexible and performant API endpoint for querying them. In this episode Artom Keydunov and Pavel Tiunov share their work on Cube.js and the various ways that it is being used in the open source community. 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

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1:05:33

Building A System Of Record For Your Organization's...

Data Engineering Podcast ·

2021/12/20

Summary Building a well managed data ecosystem for your organization requires a holistic view of all of the producers, consumers, and processors of information. The team at Metaphor are building a fully connected metadata layer to provide both technical and social intelligence about your data. In this episode Pardhu Gunnam and Mars Lan explain how they have designed the architecture and user experience to allow everyone to collaborate on the data lifecycle and provide opportunities for automation

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42:09

Building Auditable Spark Pipelines At Capital One

Data Engineering Podcast ·

2021/12/13

Summary Spark is a powerful and battle tested framework for building highly scalable data pipelines. Because of its proven ability to handle large volumes of data Capital One has invested in it for their business needs. In this episode Gokul Prabagaren shares his use for it in calculating your rewards points, including the auditing requirements and how he designed his pipeline to maintain all of the necessary information through a pattern of data enrichment. Announcements Hello and welcome to the

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57:33

Deliver Personal Experiences In Your Applications...

Data Engineering Podcast ·

2021/12/12

Summary The core to providing your users with excellent service is to understand them and provide a personalized experience. Unfortunately many sites and applications take that to the extreme and collect too much information. In order to make it easier for developers to build customer profiles in a way that respects their privacy Serge Huber helped to create the Apache Unomi framework as an open source customer data platform. In this episode he explains how it can be used to build rich and useful

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50:02

Data Driven Hiring For Data Professionals With Alooba

Data Engineering Podcast ·

2021/12/04

Summary Hiring data professionals is challenging for a multitude of reasons, and as with every interview process there is a potential for bias to creep in. Tim Freestone founded Alooba to provide a more stable reference point for evaluating candidates to ensure that you can make more informed comparisons based on their actual knowledge. In this episode he explains how Alooba got started, how it is being used in the interview process for data oriented roles, and how it can also provide visibility

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58:00

Experimentation and A/B Testing For Modern Data Teams...

Data Engineering Podcast ·

2021/12/04

Summary A/B testing and experimentation are the most reliable way to determine whether a change to your product will have the desired effect on your business. Unfortunately, being able to design, deploy, and validate experiments is a complex process that requires a mix of technical capacity and organizational involvement which is hard to come by. Chetan Sharma founded Eppo to provide a system that organizations of every scale can use to reduce the burden of managing experiments so that you can focus

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58:31

Creating A Unified Experience For The Modern Data...

Data Engineering Podcast ·

2021/11/27

Summary The modern data stack has been gaining a lot of attention recently with a rapidly growing set of managed services for different stages of the data lifecycle. With all of the available options it is possible to run a scalable, production grade data platform with a small team, but there are still sharp edges and integration challenges to work through. Peter Fishman and Dan Silberman experienced these difficulties firsthand and created Mozart Data to provide a single, easy to use option for

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59:16

Doing DataOps For External Data Sources As A Service...

Data Engineering Podcast ·

2021/11/27

Summary The data that you have access to affects the questions that you can answer. By using external data sources you can drastically increase the range of analysis that is available to your organization. The challenge comes in all of the operational aspects of finding, accessing, organizing, and serving that data. In this episode Mark Hookey discusses how he and his team at Demyst do all of the DataOps for external data sources so that you don’t have to, including the systems necessary to

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1:05:25

Laying The Foundation Of Your Data Platform For The...

Data Engineering Podcast ·

2021/11/20

Summary The technology for scaling storage and processing of data has gone through massive evolution over the past decade, leaving us with the ability to work with massive datasets at the cost of massive complexity. Nick Schrock created the Dagster framework to help tame that complexity and scale the organizational capacity for working with data. In this episode he shares the journey that he and his team at Elementl have taken to understand the state of the ecosystem and how they can provide a

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52:53

Exploring Processing Patterns For Streaming Data...

Data Engineering Podcast ·

2021/11/20

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are

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58:54

Data Quality Starts At The Source

Data Engineering Podcast ·

2021/11/14

Summary The most important gauge of success for a data platform is the level of trust in the accuracy of the information that it provides. In order to build and maintain that trust it is necessary to invest in defining, monitoring, and enforcing data quality metrics. In this episode Michael Harper advocates for proactive data quality and starting with the source, rather than being reactive and having to work backwards from when a problem is found. Announcements Hello and welcome to the Data

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1:06:54

Eliminate Friction In Your Data Platform Through...

Data Engineering Podcast ·

2021/11/10

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. After experiencing the impacts of fragmented metadata and previous attempts at building a solution Suresh Srinivas and Sriharsha Chintalapani created the OpenMetadata project. In this episode they share the lessons that they have learned through their previous attempts and the positive impact that a unified metadata layer had during

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1:02:00

Business Intelligence Beyond The Dashboard With ClicData

Data Engineering Podcast ·

2021/11/06

Summary Business intelligence is often equated with a collection of dashboards that show various charts and graphs representing data for an organization. What is overlooked in that characterization is the level of complexity and effort that are required to collect and present that information, and the opportunities for providing those insights in other contexts. In this episode Telmo Silva explains how he co-founded ClicData to bring full featured business intelligence and reporting to every

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1:02:06

Exploring The Evolution And Adoption of Customer Data...

Data Engineering Podcast ·

2021/11/05

Summary The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data. A natural outgrowth of that capability is the more recent growth of reverse ETL systems that use those analytics to feed back into the operational systems used to engage with the customer. In

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1:08:48

Removing The Barrier To Exploratory Analytics with...

Data Engineering Podcast ·

2021/10/29

Summary The perennial question of data warehousing is how to model the information that you are storing. This has given rise to methods as varied as star and snowflake schemas, data vault modeling, and wide tables. The challenge with many of those approaches is that they are optimized for answering known questions but brittle and cumbersome when exploring unknowns. In this episode Ahmed Elsamadisi shares his journey to find a more flexible and universal data model in the form of the "activity

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1:09:32

Streaming Data Pipelines Made SQL With Decodable

Data Engineering Podcast ·

2021/10/29

Summary Streaming data systems have been growing more capable and flexible over the past few years. Despite this, it is still challenging to build reliable pipelines for stream processing. In this episode Eric Sammer discusses the shortcomings of the current set of streaming engines and how they force engineers to work at an extremely low level of abstraction. He also explains why he started Decodable to address that limitation and the work that he and his team have done to let data engineers build

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1:06:02

Data Exploration For Business Users Powered By...

Data Engineering Podcast ·

2021/10/23

Summary The market for business intelligence has been going through an evolutionary shift in recent years. One of the driving forces for that change has been the rise of analytics engineering powered by dbt. Lightdash has fully embraced that shift by building an entire open source business intelligence framework that is powered by dbt models. In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already

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1:09:06

Completing The Feedback Loop Of Data Through...

Data Engineering Podcast ·

2021/10/21

Summary The focus of the past few years has been to consolidate all of the organization’s data into a cloud data warehouse. As a result there have been a number of trends in data that take advantage of the warehouse as a single focal point. Among those trends is the advent of operational analytics, which completes the cycle of data from collection, through analysis, to driving further action. In this episode Boris Jabes, CEO of Census, explains how the work of synchronizing cleaned and

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1:08:18

Bringing The Power Of The DataHub Real-Time Metadata...

Data Engineering Podcast ·

2021/10/16

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. It was also designed to be able to work for small scale systems that are just starting to develop in complexity. In order to support the project and make it even easier to use for organizations of every size Shirshanka Das and

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1:01:59

How And Why To Become Data Driven As A Business

Data Engineering Podcast ·

2021/10/14

Summary Organizations of all sizes are striving to become data driven, starting in earnest with the rise of big data a decade ago. With the never-ending growth in data sources and methods for aggregating and analyzing them, the use of data to direct the business has become a requirement. Randy Bean has been helping enterprise organizations define and execute their data strategies since before the age of big data. In this episode he discusses his experiences and how he approached the work of

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509 results

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