Cloudera

APRIL 3, 2023

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

Data Warehouse 79

Data Warehouse Snapshot Metadata Cost-Benefit 79

AWS Big Data

NOVEMBER 1, 2023

Originally published on December 9th, 2022. Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Amazon Redshift is a cloud-based data warehouse that supports many recovery capabilities to address unforeseen outages and minimize downtime.

Data Warehouse 97

Data Warehouse Snapshot Testing Management 97

AWS Big Data

JUNE 28, 2023

Amazon Aurora zero-ETL integration with Amazon Redshift was announced at AWS re:Invent 2022 and is now available in public preview for Amazon Aurora MySQL-Compatible Edition 3 (compatible with MySQL 8.0) For this illustration, we use a provisioned Aurora database and an Amazon Redshift Serverless data warehouse.

Data Warehouse 88

Data Warehouse Analytics Metrics Dashboards 88

AWS Big Data

APRIL 27, 2023

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance. Create a table to point to the CDC data.

Data Lake 75

Data Lake Snapshot Optimization Data Transformation 75

AWS Big Data

MAY 30, 2023

resource(“dynamodb”) table = dynamodb.Table(dydb_lookup_table) response = table.scan() items = response[“Items”] jsondata = sc.parallelize(items) lookupDf = glueContext.read.json(jsondata) return lookupDf # Load the Amazon Kinesis data stream from Amazon Glue Data Catalog. def readDynamoDb(): dynamodb = boto3.resource(“dynamodb”)

Data Lake 82

Data Lake Data Analytics Analytics Data Processing 82

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale. Clustering data for better data colocation using z-ordering.

Data Lake 117

Data Lake Metadata Optimization Statistics 117

AWS Big Data

MARCH 3, 2023

We use an example use case where the EMR Serverless job runs every hour, and the input data folder is partitioned on an hourly basis from AWS DMS. For more information, refer to Creating external tables for data managed in Delta Lake. A Delta table manifest contains a list of files that make up a consistent snapshot of the Delta table.

Data Lake 85

Data Lake Dashboards Metrics Metadata 85

AWS Big Data

JUNE 21, 2023

These types of queries are suited for a data warehouse. The goal of a data warehouse is to enable businesses to analyze their data fast; this is important because it means they are able to gain valuable insights in a timely manner. Amazon Redshift is fully managed, scalable, cloud data warehouse.

Data Warehouse 83

Data Warehouse Data Lake OLAP Cost-Benefit 83

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 107

Data Lake Management Metrics Data Warehouse 107