Processing high velocity time-series data in real-time is a complex challenge. The team at PipelineDB has built a continuous query engine that simplifies the task of computing aggregates across incoming streams of events. In this episode Derek Nelson and Usman Masood explain how it is architected, strategies for designing your data flows, how to scale it up and out, and edge cases to be aware of.
Apache Spark is a popular and widely used tool for a variety of data oriented projects. With the large array of capabilities, and the complexity of the underlying system, it can be difficult to understand how to get started using it. Jean George Perrin has been so impressed by the versatility of Spark that he is writing a book for data engineers to hit the ground running. In this episode he helps to make sense of what Spark is, how it works, and the various ways that you can use it. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem.
As your data needs scale across an organization the need for a carefully considered approach to collection, storage, organization, and access becomes increasingly critical. In this episode Todd Walter shares his considerable experience in data curation to clarify the many aspects that are necessary for a successful platform for your business. Using the metaphor of a museum curator carefully managing the precious resources on display and in the vaults, he discusses the various layers of an enterprise data strategy. This includes modeling the lifecycle of your information as a pipeline from the raw, messy, loosely structured records in your data lake, through a series of transformations and ultimately to your data warehouse. He also explains which layers are useful for the different members of the business, and which pitfalls to look out for along the path to a mature and flexible data platform.
Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.
Data integration and routing is a constantly evolving problem and one that is fraught with edge cases and complicated requirements. The Apache NiFi project models this problem as a collection of data flows that are created through a self-service graphical interface. This framework provides a flexible platform for building a wide variety of integrations that can be managed and scaled easily to fit your particular needs. In this episode project members Kevin Doran and Andy LoPresto discuss the ways that NiFi can be used, how to start using it in your environment, and plans for future development. They also explained how it fits in the broad landscape of data tools, the interesting and challenging aspects of the project, and how to build new extensions.
Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations.
The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Alan Anders, the CTO of Applecart about their challenges with getting Spark to scale for constructing an entity graph from multiple data sources. Next I spoke with Stepan Pushkarev, the CEO, CTO, and Co-Founder of Hydrosphere.io about the challenges of running machine learning models in production and how his team tracks key metrics and samples production data to re-train and re-deploy those models for better accuracy and more robust operation.
Cloud computing and ubiquitous virtualization have changed the ways that our applications are built and deployed. This new environment requires a new way of tracking and addressing the security of our systems. ThreatStack is a platform that collects all of the data that your servers generate and monitors for unexpected anomalies in behavior that would indicate a breach and notifies you in near-realtime. In this episode ThreatStack’s director of operations, Pete Cheslock, and senior infrastructure security engineer, Patrick Cable, discuss the data infrastructure that supports their platform, how they capture and process the data from client systems, and how that information can be used to keep your systems safe from attackers.
One of the critical components for modern data infrastructure is a scalable and reliable messaging system. Publish-subscribe systems have been popular for many years, and recently stream oriented systems such as Kafka have been rising in prominence. This week Rajan Dhabalia and Matteo Merli discuss the work they have done on Pulsar, which supports both options, in addition to being globally scalable and fast. They explain how Pulsar is architected, how to scale it, and how it fits into your existing infrastructure.
Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.