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Rockset Architecture Whiteboard Session With CTO Dhruba Borthakur

Rockset

He was an engineer on the database team at Facebook, where he was the founding engineer of the RocksDB data store. Earlier at Yahoo, he was one of the founding engineers of the Hadoop Distributed File System. He was also a contributor to the open source Apache HBase project.

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StreamNative Brings Streaming Data To The Cloud Native Landscape With Pulsar

Data Engineering Podcast

With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data?

Cloud 100
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Handling Bursty Traffic in Real-Time Analytics Applications

Rockset

Databases could just buffer, ingest and query data on a regular schedule. Finally, you could always plan ahead for bursty traffic and overprovision your database clusters and pipelines. Many databases claim to deliver scalability on demand so that you can avoid expensive overprovisioning and keep your data-driven operations humming.

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The Stream Processing Model Behind Google Cloud Dataflow

Towards Data Science

Paper’s Introduction At the time of the paper writing, data processing frameworks like MapReduce and its “cousins “ like Hadoop , Pig , Hive , or Spark allow the data consumer to process batch data at scale. On the stream processing side, tools like MillWheel , Spark Streaming , or Storm came to support the user.

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Maintaining Your Data Lake At Scale With Spark

Data Engineering Podcast

You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. The Lambda architecture was popular in the early days of Hadoop but seems to have fallen out of favor.

Data Lake 100
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Apache Spark Use Cases & Applications

Knowledge Hut

It is also friendly for database developers as it provides Spark SQL which supports most of the ANSI SQL functionality. Features of Spark Speed : According to Apache, Spark can run applications on Hadoop cluster up to 100 times faster in memory and up to 10 times faster on disk. All this processing is done using Apache Spark.

Scala 52
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20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

This data engineering project uses the following big data stack - Azure Structured Query Language (SQL) Database instance for persistent storage; to store forecasts and historical distribution data. Learn how to process Wikipedia archives using Hadoop and identify the lived pages in a day.