AWS Big Data

NOVEMBER 15, 2023

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. They can use their own toolsets or rely on provided blueprints to ingest the data from source systems.

Dashboards 95

Dashboards Analytics Metadata Data Warehouse 95

Cloudera

OCTOBER 7, 2022

We’re excited to announce the general availability of the open source adapters for dbt for all the engines in CDP — Apache Hive , Apache Impala , and Apache Spark, with added support for Apache Livy and Cloudera Data Engineering. The Open Data Lakehouse . Cloudera builds dbt adaptors for all engines in the open data lakehouse.

Data Warehouse 82

Data Warehouse Data Transformation Testing Data Lake 82

Alation

AUGUST 11, 2022

We chatted about industry trends, why decentralization has become a hot topic in the data world, and how metadata drives many data-centric use cases. But, through it all, Mohan says it’s critical to view everything through the same lens: gaining business value from data. Data fabric is a technology architecture.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Why integrate Apache Iceberg with Cloudera Data Platform?

Data Lake 89

Data Lake Data Architecture Metadata Data Warehouse 89

IBM Big Data Hub

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. Automated development: Automates data preparation, model development, feature engineering and hyperparameter optimization using AutoAI.

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

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

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 75

Testing Big Data Metadata Optimization 75

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.

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76

AWS Big Data

OCTOBER 11, 2023

The data in the machine-readable files can provide valuable insights to understand the true cost of healthcare services and compare prices and quality across hospitals. The availability of machine-readable files opens up new possibilities for data analytics, allowing organizations to analyze large amounts of pricing data.

Visualization 96

Visualization Dashboards Data-driven Gap analysis 96

Octopai

JANUARY 19, 2021

This is done by visualizing the Azure Data Factory pipelines’ full column-level with source-to-target traceability through different data transformations at the most detailed level. Octopai can fully map the BI landscape and trace metadata movement in a mixed environment including complex multi-vendor landscapes.

Metadata 52

Metadata ROI Optimization Data Quality 52

AWS Big Data

JULY 20, 2023

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. This ensures that the data is suitable for training purposes.

Data Lake 88

Data Lake Analytics Snapshot Optimization 88

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 85

Enterprise Data Warehouse Data Lake Optimization 85

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 85

Management Interactive Metadata Publishing 85

AWS Big Data

OCTOBER 18, 2023

FINRA centralizes all its data in Amazon Simple Storage Service (Amazon S3) with a remote Hive metastore on Amazon Relational Database Service (Amazon RDS) to manage their metadata information. Melody Yang is a Senior Big Data Solutions Architect for Amazon EMR at AWS. or later installed.

Big Data 81

Big Data Data Processing Interactive Testing 81

Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Source: [link] I will finish with three quotes.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

AWS Big Data

MARCH 15, 2023

The API retrieves data at runtime from an Amazon Aurora PostgreSQL-Compatible Edition database for end-user consumption. To populate the database, the Infomedia team developed a data pipeline using Amazon Simple Storage Service (Amazon S3) for data storage, AWS Glue for data transformations, and Apache Hudi for CDC and record-level updates.

Cost-Benefit 67

Cost-Benefit Data Processing Optimization Data-driven 67

Cloudera

OCTOBER 11, 2021

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

Data Lake 96

Data Lake Cost-Benefit Data-driven Dashboards 96

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. For InitialRunFlag , choose Setup.

Data Lake 86

Data Lake Data Transformation Cost-Benefit Data-driven 86

Alation

NOVEMBER 22, 2022

Before you implement a data governance framework, you need to know the data you already have. This means you need to: Inventory data: Know all information resources and relevant metadata. Classify data: Organize structured and unstructured data into relevant categories. Reuse metadata productively.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

CIO Business Intelligence

APRIL 30, 2024

Einstein Copilot for Tableau remains in beta, but Tableau announced two new features for the AI assistant as well: AI-assisted data transformation. This feature can automate a data transformation pipeline with step-by-step suggestions for preparing data for analysis.

Analytics 94

Analytics Metrics Visualization Dashboards 94

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. As a result, alternative data integration technologies (e.g.,

Data Processing 95

Data Processing Data Warehouse Enterprise Visualization 95

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

Jet Global

MAY 1, 2023

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. Metadata Self-service analysis is made easy with user-friendly naming conventions for tables and columns.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52