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Apache Spark’s fine-grained processing of RDBMS records part 4: Do SparkSQL’s native operations support the Requirements?

  • Written byadmin
  • Posted on September 15, 2018October 11, 2018

So far, we have formulated the problem we are facing: how to have Spark write data from a dataframe to a relational table, such that this operation satisfies a set of requirements? Look at it…

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Apache Spark’s fine-grained processing of RDBMS records part 3: Case Study Requirements

  • Written byadmin
  • Posted on September 13, 2018October 11, 2018

Given the background of the case study explained in details in the previous post, now we focus on the requirements of the LOAD step of the SparkSQL jobs in the pipeline 1. The 5 Requirements…

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Apache Spark’s fine-grained processing of RDBMS records part 2: Case Study Background

  • Written byadmin
  • Posted on September 11, 2018November 2, 2018

This is a long post. Reading through and getting a good understanding of it will help you follow closely the progressive development in future posts. However, the essence, the takeaway, as alluded in the Introduction…

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Apache Spark’s fine-grained processing of RDBMS records part 1: Introduction

  • Written byadmin
  • Posted on September 8, 2018October 10, 2018

At a high level, in a typical enterprise data infrastructure, there usually exists at least two relatively separate systems: a transactional system (OLTP), and an analytical system (OLAP, Data Warehouse). An ETL data pipeline would…

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Apache Spark’s fine-grained processing of RDBMS records part 0: Preface

  • Written byadmin
  • Posted on September 8, 2018October 24, 2018

I launch my blog with this fairly long post series (expected 20+ posts). I hope to write and release it gradually over the next few weeks. It might be considered of a case study discussion,…

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