Data Pipelines

Building A Knowledge Graph Of Commercial Real Estate At Cherre - Episode 127

Knowledge graphs are a data resource that can answer questions beyond the scope of traditional data analytics. By organizing and storing data to emphasize the relationship between entities, we can discover the complex connections between multiple sources of information. In this episode John Maiden talks about how Cherre builds knowledge graphs that provide powerful insights for their customers and the engineering challenges of building a scalable graph. If you’re wondering how to extract additional business value from existing data, this episode will provide a way to expand your data resources.

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Easier Stream Processing On Kafka With ksqlDB - Episode 122

Building applications on top of unbounded event streams is a complex endeavor, requiring careful integration of multiple disparate systems that were engineered in isolation. The ksqlDB project was created to address this state of affairs by building a unified layer on top of the Kafka ecosystem for stream processing. Developers can work with the SQL constructs that they are familiar with while automatically getting the durability and reliability that Kafka offers. In this episode Michael Drogalis, product manager for ksqlDB at Confluent, explains how the system is implemented, how you can use it for building your own stream processing applications, and how it fits into the lifecycle of your data infrastructure. If you have been struggling with building services on low level streaming interfaces then give this episode a listen and try it out for yourself.

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Pay Down Technical Debt In Your Data Pipeline With Great Expectations - Episode 117

Data pipelines are complicated and business critical pieces of technical infrastructure. Unfortunately they are also complex and difficult to test, leading to a significant amount of technical debt which contributes to slower iteration cycles. In this episode James Campbell describes how he helped create the Great Expectations framework to help you gain control and confidence in your data delivery workflows, the challenges of validating and monitoring the quality and accuracy of your data, and how you can use it in your own environments to improve your ability to move fast.

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Automating Your Production Dataflows On Spark - Episode 105

As data engineers the health of our pipelines is our highest priority. Unfortunately, there are countless ways that our dataflows can break or degrade that have nothing to do with the business logic or data transformations that we write and maintain. Sean Knapp founded Ascend to address the operational challenges of running a production grade and scalable Spark infrastructure, allowing data engineers to focus on the problems that power their business. In this episode he explains the technical implementation of the Ascend platform, the challenges that he has faced in the process, and how you can use it to simplify your dataflow automation. This is a great conversation to get an understanding of all of the incidental engineering that is necessary to make your data reliable.

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Keeping Your Data Warehouse In Order With DataForm - Episode 102

Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team’s communication. In this episode CTO and co-founder of Dataform Lewis Hemens joins the show to explain his motivation for creating the platform and company, how it works under the covers, and how you can start using it today to get your data warehouse under control.

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Ship Faster With An Opinionated Data Pipeline Framework - Episode 100

Building an end-to-end pipeline for your machine learning projects is a complex task, made more difficult by the variety of ways that you can structure it. Kedro is a framework that provides an opinionated workflow that lets you focus on the parts that matter, so that you don’t waste time on gluing the steps together. In this episode Tom Goldenberg explains how it works, how it is being used at Quantum Black for customer projects, and how it can help you structure your own. Definitely worth a listen to gain more understanding of the benefits that a standardized process can provide.

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Navigating Boundless Data Streams With The Swim Kernel - Episode 98

The conventional approach to analytics involves collecting large amounts of data that can be cleaned, followed by a separate step for analysis and interpretation. Unfortunately this strategy is not viable for handling real-time, real-world use cases such as traffic management or supply chain logistics. In this episode Simon Crosby, CTO of Swim Inc., explains how the SwimOS kernel and the enterprise data fabric built on top of it enable brand new use cases for instant insights. This was an eye opening conversation about how stateful computation of data streams from edge devices can reduce cost and complexity as compared to batch oriented workflows.

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Building A Reliable And Performant Router For Observability Data - Episode 97

The first stage in every data project is collecting information and routing it to a storage system for later analysis. For operational data this typically means collecting log messages and system metrics. Often a different tool is used for each class of data, increasing the overall complexity and number of moving parts. The engineers at Timber.io decided to build a new tool in the form of Vector that allows for processing both of these data types in a single framework that is reliable and performant. In this episode Ben Johnson and Luke Steensen explain how the project got started, how it compares to other tools in this space, and how you can get involved in making it even better.

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Building Tools And Platforms For Data Analytics - Episode 95

Data engineers are responsible for building tools and platforms to power the workflows of other members of the business. Each group of users has their own set of requirements for the way that they access and interact with those platforms depending on the insights they are trying to gather. Benn Stancil is the chief analyst at Mode Analytics and in this episode he explains the set of considerations and requirements that data analysts need in their tools and. He also explains useful patterns for collaboration between data engineers and data analysts, and what they can learn from each other.

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A High Performance Platform For The Full Big Data Lifecycle - Episode 94

Managing big data projects at scale is a perennial problem, with a wide variety of solutions that have evolved over the past 20 years. One of the early entrants that predates Hadoop and has since been open sourced is the HPCC (High Performance Computing Cluster) system. Designed as a fully integrated platform to meet the needs of enterprise grade analytics it provides a solution for the full lifecycle of data at massive scale. In this episode Flavio Villanustre, VP of infrastructure and products at HPCC Systems, shares the history of the platform, how it is architected for scale and speed, and the unique solutions that it provides for enterprise grade data analytics. He also discusses the motivations for open sourcing the platform, the detailed workflow that it enables, and how you can try it for your own projects. This was an interesting view of how a well engineered product can survive massive evolutionary shifts in the industry while remaining relevant and useful.

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