Cloudera

AUGUST 17, 2021

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Let’s take a common use-case for Business Intelligence reporting. Figure 2: Example BI reporting data pipeline.

Management 115

Management Cost-Benefit Data Transformation Optimization 115

IBM Big Data Hub

MARCH 14, 2024

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging. With Netezza support for 1.2

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76

IBM Big Data Hub

JULY 17, 2023

IBM software products are embedding watsonx capabilities across digital labor, IT automation, security, sustainability, and application modernization to help unlock new levels of business value for clients. In this blog, I will cover: What is watsonx.ai? What capabilities are included in watsonx.ai? What is watsonx.data?

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

IBM Big Data Hub

JANUARY 18, 2023

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

AWS Big Data

JULY 20, 2023

This data is then used by various applications for streaming analytics, business intelligence, and reporting. Apache Iceberg’s hidden partitions combined with partition transformations proved to be valuable in achieving this goal because it allowed for transparent changes to partitioning without impacting end-users.

Data Lake 87

Data Lake Analytics Snapshot Optimization 87

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. The right data model + artificial intelligence = augmented analytics. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

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 102

Data Lake Management Metrics Data Warehouse 102

AWS Big Data

AUGUST 1, 2023

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Choose Update.

Data Lake 85

Data Lake Data Transformation Cost-Benefit Data-driven 85

AWS Big Data

SEPTEMBER 19, 2023

However, our legacy data warehouse-based solution was not equipped for this challenge. It was designed to manage complex queries and business intelligence (BI) use cases on a large scale. This enabled us to track down less optimized jobs and work with job owners to implement best practices with impact-based priority.

Analytics 78

Analytics Risk Big Data Machine Learning 78