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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

Amazon S3 allows you to access diverse data sets, build business intelligence dashboards, and accelerate the consumption of data by adopting a modern data architecture or data mesh pattern on Amazon Web Services (AWS). In this method, the metadata are recreated in an isolated environment and colocated with the existing data files.

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CRM’s Have a Big Data Technical Debt Problem: Here’s How to Fix It

Smart Data Collective

Metazoa is the company behind the Salesforce ecosystem’s top software toolset for org management, Metazoa Snapshot. Created in 2006, Snapshot was the first CRM management solution designed specifically for Salesforce and was one of the first Apps to be offered on the Salesforce AppExchange. Unused assets.

Big Data 131
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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Through Amazon Redshift in-memory result set caching and compilation caching, workloads ranging from dashboarding to visualization to business intelligence (BI) that run repeat queries experience a significant performance boost. Chargeback metadata Amazon Redshift provides different pricing models to cater to different customer needs.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

One important feature is to run different workloads such as business intelligence (BI), Machine Learning (ML), Data Science and data exploration, and Change Data Capture (CDC) of transactional data, without having to maintain multiple copies of data. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 106
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Materialized Views in Hive for Iceberg Table Format

Cloudera

Materialized views are valuable for accelerating common classes of business intelligence (BI) queries that consist of joins, group-bys and aggregate functions. The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Furthermore, it is partitioned on the d_year column.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Frequent materialized view refreshes on top of constantly changing base tables due to streamed data can lead to snapshot isolation errors. Datasets used for generating insights are curated using materialized views inside the database and published for business intelligence (BI) reporting.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

Iceberg doesn’t optimize file sizes or run automatic table services (for example, compaction or clustering) when writing, so streaming ingestion will create many small data and metadata files. Offers different query types , allowing to prioritize data freshness (Snapshot Query) or read performance (Read Optimized Query).

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