As more companies and organizations are working to gain a real-time view of their business, they are increasingly turning to stream processing technologies to fullfill that need. However, the storage requirements for continuous, unbounded streams of data are markedly different than that of batch oriented workloads. To address this shortcoming the team at Dell EMC has created the open source Pravega project. In this episode Tom Kaitchuk explains how Pravega simplifies storage and processing of data streams, how it integrates with processing engines such as Flink, and the unique capabilities that it provides in the area of exactly once processing and transactions. And if you listen at approximately the half-way mark, you can hear as the hosts mind is blown by the possibilities of treating everything, including schema information, as a stream.
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.
Distributed systems are complex to build and operate, and there are certain primitives that are common to a majority of them. Rather then re-implement the same capabilities every time, many projects build on top of Apache Zookeeper. In this episode Patrick Hunt explains how the Apache Zookeeper project was started, how it functions, and how it is used as a building block for other distributed systems. He also explains the operational considerations for running your own cluster, how it compares to more recent entrants such as Consul and EtcD, and what is in store for the future.
When your data lives in multiple locations, belonging to at least as many applications, it is exceedingly difficult to ask complex questions of it. The default way to manage this situation is by crafting pipelines that will extract the data from source systems and load it into a data lake or data warehouse. In order to make this situation more manageable and allow everyone in the business to gain value from the data the folks at Dremio built a self service data platform. In this episode Tomer Shiran, CEO and co-founder of Dremio, explains how it fits into the modern data landscape, how it works under the hood, and how you can start using it today to make your life easier.
Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how you can start using it today.
With the growth of the Hadoop ecosystem came a proliferation of implementations for the Hive table format. Unfortunately, with no formal specification, each project works slightly different which increases the difficulty of integration across systems. The Hive format is also built with the assumptions of a local filesystem which results in painful edge cases when leveraging cloud object storage for a data lake. In this episode Ryan Blue explains how his work on the Iceberg table format specification and reference implementation has allowed Netflix to improve the performance and simplify operations for their S3 data lake. This is a highly detailed and technical exploration of how a well-engineered metadata layer can improve the speed, accuracy, and utility of large scale, multi-tenant, cloud-native data platforms.
One of the most complex aspects of managing data for analytical workloads is moving it from a transactional database into the data warehouse. What if you didn’t have to do that at all? MemSQL is a distributed database built to support concurrent use by transactional, application oriented, and analytical, high volume, workloads on the same hardware. In this episode the CEO of MemSQL describes how the company and database got started, how it is architected for scale and speed, and how it is being used in production. This was a deep dive on how to build a successful company around a powerful platform, and how that platform simplifies operations for enterprise grade data management.
The way that you store your data can have a huge impact on the ways that it can be practically used. For a substantial number of use cases, the optimal format for storing and querying that information is as a graph, however databases architected around that use case have historically been difficult to use at scale or for serving fast, distributed queries. In this episode Manish Jain explains how DGraph is overcoming those limitations, how the project got started, and how you can start using it today. He also discusses the various cases where a graph storage layer is beneficial, and when you would be better off using something else. In addition he talks about the challenges of building a distributed, consistent database and the tradeoffs that were made to make DGraph a reality.
When working with large volumes of data that you need to access in parallel across multiple instances you need a distributed filesystem that will scale with your workload. Even better is when that same system provides multiple paradigms for interacting with the underlying storage. Ceph is a highly available, highly scalable, and performant system that has support for object storage, block storage, and native filesystem access. In this episode Sage Weil, the creator and lead maintainer of the project, discusses how it got started, how it works, and how you can start using it on your infrastructure today. He also explains where it fits in the current landscape of distributed storage and the plans for future improvements.
With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today.