Real Time

Making Analytical APIs Fast With Tinybird - Episode 185

Building an API for real-time data is a challenging project. Making it robust, scalable, and fast is a full time job. The team at Tinybird wants to make it easy to turn a continuous stream of data into a production ready API or data product. In this episode CEO Jorge Sancha explains how they have architected their system to handle high data throughput and fast response times, and why they have invested heavily in Clickhouse as the core of their platform. This is a great conversation about the challenges of building a maintainable business from a technical and product perspective.

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Real World Change Data Capture At Datacoral - Episode 177

The world of business is becoming increasingly dependent on information that is accurate up to the minute. For analytical systems, the only way to provide this reliably is by implementing change data capture (CDC). Unfortunately, this is a non-trivial undertaking, particularly for teams that don’t have extensive experience working with streaming data and complex distributed systems. In this episode Raghu Murthy, founder and CEO of Datacoral, does a deep dive on how he and his team manage change data capture pipelines in production.

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Building Real Time Applications On Streaming Data With Eventador - Episode 129

Modern applications frequently require access to real-time data, but building and maintaining the systems that make that possible is a complex and time consuming endeavor. Eventador is a managed platform designed to let you focus on using the data that you collect, without worrying about how to make it reliable. In this episode Eventador Founder and CEO Kenny Gorman describes how the platform is architected, the challenges inherent to managing reliable streams of data, the simplicity offered by a SQL interface, and the interesting projects that his customers have built on top of it. This was an interesting inside look at building a business on top of open source stream processing frameworks and how to reduce the burden on end users.

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Building The DataDog Platform For Processing Timeseries Data At Massive Scale - Episode 113

DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale. Getting an inside look at the companies behind the services we use is always useful, and this conversation was no exception.

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Building A Real Time Event Data Warehouse For Sentry - Episode 108

The team at Sentry has built a platform for anyone in the world to send software errors and events. As they scaled the volume of customers and data they began running into the limitations of their initial architecture. To address the needs of their business and continue to improve their capabilities they settled on Clickhouse as the new storage and query layer to power their business. In this episode James Cunningham and Ted Kaemming describe the process of rearchitecting a production system, what they learned in the process, and some useful tips for anyone else evaluating Clickhouse.

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TimescaleDB: The Timeseries Database Built For SQL And Scale - Episode 65

The past year has been an active one for the timeseries market. New products have been launched, more businesses have moved to streaming analytics, and the team at Timescale has been keeping busy. In this episode the TimescaleDB CEO Ajay Kulkarni and CTO Michael Freedman stop by to talk about their 1.0 release, how the use cases for timeseries data have proliferated, and how they are continuing to simplify the task of processing your time oriented events.

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Simplifying Continuous Data Processing Using Stream Native Storage In Pravega with Tom Kaitchuck - Episode 63

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.

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Continuously Query Your Time-Series Data Using PipelineDB with Derek Nelson and Usman Masood - Episode 62

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.

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Pulsar: Fast And Scalable Messaging with Rajan Dhabalia and Matteo Merli - Episode 17

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.

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Wallaroo with Sean T. Allen - Episode 12

Data oriented applications that need to operate on large, fast-moving sterams of information can be difficult to build and scale due to the need to manage their state. In this episode Sean T. Allen, VP of engineering for Wallaroo Labs, explains how Wallaroo was designed and built to reduce the cognitive overhead of building this style of project. He explains the motivation for building Wallaroo, how it is implemented, and how you can start using it today.

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