CIO Business Intelligence

JUNE 29, 2022

If you can’t make sense of your business data, you’re effectively flying blind. Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products — or new lines of business, like predictive maintenance. Azure Data Factory.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 87

Enterprise Data Warehouse Data Lake Optimization 87

IBM Big Data Hub

JANUARY 18, 2023

A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Value of the data projects are difficult to realize.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. You can use it for big data analytics and machine learning workloads.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

O'Reilly on Data

OCTOBER 19, 2021

ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. With these five layers, we can present a highly productive, data-centric software interface that enables iterative development of large-scale data-intensive applications.

IT 346

IT Testing Experimentation Software 346

IBM Big Data Hub

JULY 17, 2023

.” Sean Im, CEO, Samsung SDS America “In the field of generative AI and foundation models, watsonx is a platform that will enable us to meet our customers’ requirements in terms of optimization and security, while allowing them to benefit from the dynamism and innovations of the open-source community.”

Machine Learning 66

Machine Learning Data Warehouse Modeling Cost-Benefit 66

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 82

Cost-Benefit Metadata Optimization Management 82

AWS Big Data

OCTOBER 27, 2023

Efficiency : Data transformation tasks that previously took weeks or months can now be accomplished within minutes, optimizing efficiency. He is driving the connectivity charter which provide Glue customer native way of connecting any Data source (Data-warehouse, Data-lakes, NoSQL etc) to Glue ETL Jobs.

Analytics 99

Analytics Visualization Data Integration Cost-Benefit 99

AWS Big Data

MARCH 3, 2023

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes.

Data Lake 84

Data Lake Dashboards Metrics Metadata 84

AWS Big Data

MARCH 3, 2023

From detailed design to a beta release, Tricentis had customers expecting to consume data from a data lake specific to only their data, and all of the data that had been generated for over a decade. Data export As stated earlier, some customers want to get an export of their test data and create their data lake.

Software 52

Software Data Lake Testing Cost-Benefit 52

Sisense

MAY 11, 2021

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. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!

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 106

Data Lake Management Metrics Data Warehouse 106

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. Search for the Jira Cloud connector.

Data Lake 86

Data Lake Data Transformation Cost-Benefit Data-driven 86

IBM Big Data Hub

JULY 5, 2023

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

Data Lake 66

Data Lake Metadata Cost-Benefit Data Warehouse 66

AWS Big Data

DECEMBER 21, 2023

To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse. In this post, we show how smava optimized their data platform by using Amazon Redshift Serverless and Amazon Redshift data sharing to overcome right-sizing challenges for unpredictable workloads and further improve price-performance.

Data Lake 92

Data Lake Data Warehouse Data-driven B2B 92

IBM Big Data Hub

JUNE 15, 2023

It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. To help organizations scale AI workloads, we recently announced IBM watsonx.data , a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform.

Data Warehouse 95

Data Warehouse Data Lake Optimization Data-driven 95

CIO Business Intelligence

AUGUST 3, 2023

It also used device data to develop Lenovo Device Intelligence, which uses AI-driven predictive analytics to help customers understand and proactively prevent and solve potential IT issues. Lenovo Device Intelligence can also help to optimize IT support costs, reduce employee downtime, and improve the user experience, the company says.

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. CRM platforms).

Data Processing 89

Data Processing Data Warehouse Enterprise Visualization 89

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

FEBRUARY 23, 2024

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52