Cloudera

NOVEMBER 29, 2021

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. . The company needed a modern data architecture to manage the growing traffic effectively. .

Data Lake 76

Data Lake Data Warehouse Data Architecture Reporting 76

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 81

Data Lake Dashboards Metrics Metadata 81

CIO Business Intelligence

NOVEMBER 7, 2023

Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. The aim is to normalize, aggregate, and eventually make available to analysts across the organization data that originates in various pockets of the enterprise.

Data Warehouse 142

Data Warehouse Digital Transformation Reporting Data-driven 142

Birst BI

FEBRUARY 25, 2020

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead. What gaps does the joint solution address in the market?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Architecture Enterprise 52

CIO Business Intelligence

AUGUST 3, 2023

As well as keeping its current data accurate and accessible, the company wants to leverage decades of historical data to identify potential risks to ship operations and opportunities for improvement. Each of the acquired companies had multiple data sets with different primary keys, says Hepworth. “We

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 87

Data Lake Data Warehouse B2B Data-driven 87

Ontotext

FEBRUARY 12, 2024

For many organizations, a centralized data platform will fall short as it gives data teams much less autonomy over managing increasingly diverse and voluminous datasets. A centralized data engineering team focuses on building a governed self-serviced infrastructure, while domain teams use the services to build full-stack data products.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data.

Data Lake 67

Data Lake Metadata Cost-Benefit Data Warehouse 67

Jet Global

MAY 1, 2023

These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. Data Environment First off, the solutions you consider should be compatible with your current data architecture. addresses).

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52