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

erwin

MARCH 26, 2020

There are three technological advances driving this data consumption and, in turn, the ability for employees to leverage this data to deliver business value 1) exploding data production 2) scalable big data computation, and 3) the accessibility of advanced analytics, machine learning (ML) and artificial intelligence (AI).

Data-driven 90

Data-driven Unstructured Data Enterprise Finance 90

AWS Big Data

APRIL 12, 2023

Also, you can run other types of business applications, such as web applications and machine learning (ML) TensorFlow workloads, on the same EKS cluster. We have been continually improving the Spark performance in each Amazon EMR release to further shorten job runtime and optimize users’ spending on their Amazon EMR big data workloads.

Testing 77

Testing Big Data Metadata Optimization 77

AWS Big Data

FEBRUARY 6, 2023

The most common use case for Airflow is ETL (extract, transform, and load). Operationalizing machine learning (ML) is another growing use case, where data has to be transformed and normalized before it can be loaded into an ML model. format(S3_BUCKET_NAME), 's3://{}/data/aggregated/green'.format(S3_BUCKET_NAME),

Management 88

Management Interactive Metadata Publishing 88

IBM Big Data Hub

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Sisense

OCTOBER 15, 2020

As real-time analytics and machine learning stream processing are growing rapidly, they introduce a new set of technological and conceptual challenges. Finally, click “Publish” in the upper right hand corner, and you’re ready to create a dashboard! This is where we’ll create a new dashboard to view and explore the data.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 74

Sales Visualization Software Marketing 74

Alation

NOVEMBER 22, 2022

With data governance, public sector entities can publish data ethically, using open-data portals and giving citizens visibility into how their data is used. Modernizes Data Systems. Data governance enables structured and secure data exchanges for systems built on the premise of privacy-by-design.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

AWS Big Data

JULY 26, 2023

At the time of publishing of this post, the AWS CDK has two versions of the AWS Glue module: @aws-cdk/aws-glue and @aws-cdk/aws-glue-alpha , containing L1 constructs and L2 constructs , respectively. Then you will see a view similar to the following screenshot.

Data Integration 74

Data Integration Snapshot Testing Visualization 74

Jet Global

MAY 1, 2023

Strategic Objective Create a complete, user-friendly view of the data by preparing it for analysis. Requirement Multi-Source Data Blending Data from multiple sources is compiled and the output is a single view, metric, or visualization. Data Transformation and Enrichment Data can be enriched for analysis.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52