Cloudera

DECEMBER 21, 2021

We are excited to offer in Tech Preview this born-in-the-cloud table format that will help future proof data architectures at many of our public cloud customers. This enabled new use-cases with customers that were using a mix of Spark and Hive to perform data transformations. . Modernizing pipelines. Happy New Year.

Snapshot 115

Snapshot Data-driven Optimization Management 115

AWS Big Data

NOVEMBER 15, 2023

Bayerische Motoren Werke AG (BMW) is a motor vehicle manufacturer headquartered in Germany with 149,475 employees worldwide and the profit before tax in the financial year 2022 was € 23.5 The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse.

Dashboards 98

Dashboards Analytics Metadata Data Warehouse 98

Cloudera

JUNE 30, 2022

These tools empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and analytic engines. No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data.

Data Lake 83

Data Lake Data Architecture Metadata Data Warehouse 83

AWS Big Data

MARCH 3, 2023

Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9 Monjumi Sarma is a Data Lab Solutions Architect at AWS.

Data Lake 85

Data Lake Dashboards Metrics Metadata 85

AWS Big Data

DECEMBER 21, 2023

Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3).

Data Lake 93

Data Lake Data Warehouse Data-driven B2B 93

AWS Big Data

APRIL 5, 2023

The company also used the opportunity to reimagine its data pipeline and architecture. A key architectural decision that Showpad took during this time was to create a portable data layer by decoupling the data transformation from visualization, ML, or ad hoc querying tools and centralizing its business logic.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52