Introduction The industry has been struggling for a long time with defining a data lake. We are taking the plunge, let’s properly define a data lake. I have seen hundreds of different definitions around the world, and none of them seem to provide an organization with the foundations they need to build a successful data lake.
By Cynthia Meyersohn To close out this series of articles that have been focused on the data replication introduced by the processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29, 2017, on the AWS Big Data Blog, https://aws.amazon.com/blogs/big-data/build-a-schema-on-read-analytics-pipeline-using-amazon-athena/ (Ratan, 2017), I will talk about an approach that I
By Cynthia Meyersohn Picking up from Part 2 of this series where we left off having replicated the data a minimum of nine times, we will continue to identify additional data replication stages as we trace through the data processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29,
By Cynthia Meyersohn Continuing from Part 1 of this series, this article is following the breakdown of the AWS Schema-on-Read analytics pipeline with a focus on data movement and replication. You may recall we are tracing through the data processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29,
By Cynthia Meyersohn Last August I was asked to participate in an exercise to assess whether or not the Data Vault 2.0 System of Business Intelligence (DV2) still held value. Specifically, I was asked the following: Does the size of the data now being delivered to the business make a compelling argument that the data
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Introduction The industry has been struggling for a long time with defining a data lake. We are taking the plunge, let’s properly define a data lake. I have seen hundreds of different definitions around the world, and none of them seem to provide an organization with the foundations they need to build a successful data lake….
By Cynthia Meyersohn To close out this series of articles that have been focused on the data replication introduced by the processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29, 2017, on the AWS Big Data Blog, https://aws.amazon.com/blogs/big-data/build-a-schema-on-read-analytics-pipeline-using-amazon-athena/ (Ratan, 2017), I will talk about an approach that I…
By Cynthia Meyersohn Picking up from Part 2 of this series where we left off having replicated the data a minimum of nine times, we will continue to identify additional data replication stages as we trace through the data processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29,…
By Cynthia Meyersohn Continuing from Part 1 of this series, this article is following the breakdown of the AWS Schema-on-Read analytics pipeline with a focus on data movement and replication. You may recall we are tracing through the data processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29,…
By Cynthia Meyersohn Last August I was asked to participate in an exercise to assess whether or not the Data Vault 2.0 System of Business Intelligence (DV2) still held value. Specifically, I was asked the following: Does the size of the data now being delivered to the business make a compelling argument that the data…