Data Engineering Podcast

How Upsolver Is Building A Data Lake Platform In The Cloud with Yoni Iny - Episode 56

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Summary

A data lake can be a highly valuable resource, as long as it is well built and well managed. Unfortunately, that can be a complex and time-consuming effort, requiring specialized knowledge and diverting resources from your primary business. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful data lake project, how the Upsolver platform is architected, and how modern data lakes can benefit your organization.

Preamble
  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Yoni Iny about Upsolver, a data lake platform that lets developers integrate and analyze streaming data with ease
    • Interview
    • Introduction
    • How did you get involved in the area of data management?
    • Can you start by describing what Upsolver is and how it got started?
      • What are your goals for the platform?


      • There are a lot of opinions on both sides of the data lake argument. When is it the right choice for a data platform?

        • What are the shortcomings of a data lake architecture?


        • How is Upsolver architected?

          • How has that architecture changed over time?
          • How do you manage schema validation for incoming data?
          • What would you do differently if you were to start over today?


          • What are the biggest challenges at each of the major stages of the data lake?

          • What is the workflow for a user of Upsolver and how does it compare to a self-managed data lake?

          • When is Upsolver the wrong choice for an organization considering implementation of a data platform?

          • Is there a particular scale or level of data maturity for an organization at which they would be better served by moving management of their data lake in house?

          • What features or improvements do you have planned for the future of Upsolver?

            • Contact Info
            • Yoni
              • yoniiny on GitHub
              • LinkedIn


              • Upsolver

                • Website
                • @upsolver on Twitter
                • LinkedIn
                • Facebook


                  • Parting Question
                  • From your perspective, what is the biggest gap in the tooling or technology for data management today?
                    • Links
                    • Upsolver
                    • Data Lake
                    • Israeli Army
                    • Data Warehouse
                    • Data Engineering Podcast Episode About Data Curation
                    • Three Vs
                    • Kafka
                    • Spark
                    • Presto
                    • Drill
                    • Spot Instances
                    • Object Storage
                    • Cassandra
                    • Redis
                    • Latency
                    • Avro
                    • Parquet
                    • ORC
                    • Data Engineering Podcast Episode About Data Serialization Formats
                    • SSTables
                    • Run Length Encoding
                    • CSV (Comma Separated Values)
                    • Protocol Buffers
                    • Kinesis
                    • ETL
                    • DevOps
                    • Prometheus
                    • Cloudwatch
                    • DataDog
                    • InfluxDB
                    • SQL
                    • Pandas
                    • Confluent
                    • KSQL

                      • The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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