Data Infrastructure | Data Engineering Podcast

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

September 10, 2019

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.

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

August 19, 2019

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.

Solving Data Discovery At Lyft - Episode 92

August 5, 2019

Data is only valuable if you use it for something, and the first step is knowing that it is available. As organizations grow and data sources proliferate it becomes difficult to keep track of everything, particularly for analysts and data scientists who are not involved with the collection and management of that information. Lyft has build the Amundsen platform to address the problem of data discovery and in this episode Tao Feng and Mark Grover explain how it works, why they built it, and how it has impacted the workflow of data professionals in their organization. If you are struggling to realize the value of your information because you don’t know what you have or where it is then give this a listen and then try out Amundsen for yourself.

Simplifying Data Integration Through Eventual Connectivity - Episode 91

July 29, 2019

The ETL pattern that has become commonplace for integrating data from multiple sources has proven useful, but complex to maintain. For a small number of sources it is a tractable problem, but as the overall complexity of the data ecosystem continues to expand it may be time to identify new ways to tame the deluge of information. In this episode Tim Ward, CEO of CluedIn, explains the idea of eventual connectivity as a new paradigm for data integration. Rather than manually defining all of the mappings ahead of time, we can rely on the power of graph databases and some strategic metadata to allow connections to occur as the data becomes available. If you are struggling to maintain a tangle of data pipelines then you might find some new ideas for reducing your workload.

Straining Your Data Lake Through A Data Mesh - Episode 90

July 22, 2019

The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access. In this episode Zhamak Dehghani shares an alternative approach in the form of a data mesh. Rather than connecting all of your data flows to one destination, empower your individual business units to create data products that can be consumed by other teams. This was an interesting exploration of a different way to think about the relationship between how your data is produced, how it is used, and how to build a technical platform that supports the organizational needs of your business.

Scale Your Analytics On The Clickhouse Data Warehouse - Episode 88

July 8, 2019

The market for data warehouse platforms is large and varied, with options for every use case. ClickHouse is an open source, column-oriented database engine built for interactive analytics with linear scalability. In this episode Robert Hodges and Alexander Zaitsev explain how it is architected to provide these features, the various unique capabilities that it provides, and how to run it in production. It was interesting to learn about some of the custom data types and performance optimizations that are included.

Stress Testing Kafka And Cassandra For Real-Time Anomaly Detection - Episode 87

July 2, 2019

Anomaly detection is a capability that is useful in a variety of problem domains, including finance, internet of things, and systems monitoring. Scaling the volume of events that can be processed in real-time can be challenging, so Paul Brebner from Instaclustr set out to see how far he could push Kafka and Cassandra for this use case. In this interview he explains the system design that he tested, his findings for how these tools were able to work together, and how they behaved at different orders of scale. It was an interesting conversation about how he stress tested the Instaclustr managed service for benchmarking an application that has real-world utility.