datapine

AUGUST 20, 2019

Operations data: Data generated from a set of operations such as orders, online transactions, competitor analytics, sales data, point of sales data, pricing data, etc. The gigantic evolution of structured, unstructured, and semi-structured data is referred to as Big data. Videos, pictures etc.

Big Data 100

Big Data B2B Cost-Benefit Structured Data 100

Ontotext

APRIL 10, 2024

Knowledge graphs and semantic metadata Knowledge graphs (KGs) are the key to: Advanced Data Architecture & Models like Data Fabric, Data Mesh Unified Data Access Semantic Data Integration These fundamental capabilities of KGs enable them to bridge the chasm between information and knowledge in the DIKW pyramid.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

AWS Big Data

JANUARY 12, 2024

This solution is suitable for customers who don’t require real-time ingestion to OpenSearch Service and plan to use data integration tools that run on a schedule or are triggered through events. Before data records land on Amazon S3, we implement an ingestion layer to bring all data streams reliably and securely to the data lake.

Data Lake 94

Data Lake Cost-Benefit Visualization Structured Data 94

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

Ontotext

JULY 26, 2019

Whether you refer to the use of semantic technology as Linked Data technology or smart data management technology, these concepts boil down to connectivity. Connectivity in the sense of connecting data from different sources and assigning these data additional machine-readable meaning. Read more at: [link].

Technology 75

Technology Enterprise Data Integration Management 75

BizAcuity

MAY 24, 2022

This is done to gain better visibility of the operations, and capture data points of interest for the clients. Reasons may vary from business to business but integration is the cornerstone for customer success. With cloud data integration, it gets easier to make reports across departments and data storage will never be an issue.

Data-driven 52

Data-driven Cost-Benefit Digital Transformation Strategy 52

Ontotext

JULY 26, 2019

Whether you refer to the use of semantic technology as Linked Data technology or smart data management technology, these concepts boil down to connectivity. Connectivity in the sense of connecting data from different sources and assigning these data additional machine-readable meaning. Read more at: [link].

Technology 40

Technology Data Integration Enterprise Structured Data 40

AWS Big Data

AUGUST 31, 2023

To upgrade your existing Athena engine to version 3 in your Athena workgroup, follow the instructions in Upgrade to Athena engine version 3 to increase query performance and access more analytics features or refer to Changing the engine version in the Athena console. For more details on Iceberg format versions, refer to Format Versioning.

Data Lake 93

Data Lake Data Warehouse Metadata Data Architecture 93

Ontotext

JANUARY 10, 2024

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

AWS Big Data

NOVEMBER 13, 2023

As Gameskraft’s portfolio of gaming products increased, it led to an approximate five-times growth of dedicated data analytics and data science teams. Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster.

Data Warehouse 92

Data Warehouse Data Lake Analytics Data Science 92

AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. Various data stores are supported in AWS Glue; for example, AWS Glue 4.0

Data Lake 112

Data Lake Data Warehouse Visualization Snapshot 112

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 1: Multi-function analytics . The *Any*-house.

Metadata 92

Metadata Data Architecture Machine Learning Cost-Benefit 92

Ontotext

FEBRUARY 7, 2024

However, what we usually don’t talk about when generating an asset, are the huge invisible or unplanned costs occurring at a later stage when the data needs to be made available for analysis or secondary usage. As a result, a big portion of the IT capacity in Pharma is bound by data integration.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

AWS Big Data

SEPTEMBER 7, 2023

This blog post presents an architecture solution that allows customers to extract key insights from Amazon S3 access logs at scale. We will partition and format the server access logs with Amazon Web Services (AWS) Glue , a serverless data integration service, to generate a catalog for access logs and create dashboards for insights.

Metadata 99

Metadata Dashboards Metrics Visualization 99

AWS Big Data

NOVEMBER 29, 2023

For more information about performance improvement capabilities, refer to the list of announcements below. Neeraja is a seasoned Product Management and GTM leader, bringing over 20 years of experience in product vision, strategy and leadership roles in data products and platforms.

Data Warehouse 101

Data Warehouse Data Lake Analytics Machine Learning 101

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

Alation

JANUARY 25, 2022

The data catalog is a foundational layer of the data fabric. This zoomed-in version has references to corresponding vendor markets removed.). Using this diagram as our guide, this blog will deep-dive into each layer of the data fabric, starting with the data catalog. But what does integration look like in action?

Metadata 52

Metadata IT Metrics Data-driven 52

AWS Big Data

JUNE 7, 2023

For this, Cargotec built an Amazon Simple Storage Service (Amazon S3) data lake and cataloged the data assets in AWS Glue Data Catalog. They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed.

Metadata 55

Metadata Data Lake Machine Learning Big Data 55

Andrew White

JANUARY 9, 2022

Most of D&A concerns and activities are done within EA in the Info/Data architecture domain/phases. Much as the analytics world shifted to augmented analytics, the same is happening in data management. Here is a suggested note: Use Gartner’s Reference Model to Deliver Intelligent Composable Business Applications.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Ontotext

FEBRUARY 26, 2024

Bad Data Tax and the Data Bill of Rights So far, our discussion has been pretty theoretical, so we need a compelling business justification for moving in this direction. In the race to become data-driven, most efforts have resulted in a tangled web of data integrations and reconciliations across a sea of data silos.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

AWS Big Data

APRIL 20, 2023

In a modern data architecture, unified analytics enable you to access the data you need, whether it’s stored in a data lake or a data warehouse. One of the most common use cases for data preparation on Amazon Redshift is to ingest and transform data from different data stores into an Amazon Redshift data warehouse.

Visualization 79

Visualization Data Warehouse Big Data Data Lake 79

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. Data democratization uses a fit-for-purpose data architecture that is designed for the way today’s businesses operate, in real-time.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

AWS Big Data

DECEMBER 18, 2023

Amazon Redshift powers data-driven decisions for tens of thousands of customers every day with a fully managed, AI-powered cloud data warehouse, delivering the best price-performance for your analytics workloads. Discover how you can use Amazon Redshift to build a data mesh architecture to analyze your data.

Data Warehouse 99

Data Warehouse Machine Learning Data-driven Data Lake 99

Cloudera

OCTOBER 7, 2021

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. Components of a Data Mesh. How CDF enables successful Data Mesh Architectures.

Metadata 124

Metadata Cost-Benefit Enterprise Interactive 124

AWS Big Data

SEPTEMBER 21, 2023

Satori accelerates implementing data security controls on datawarehouses like Amazon Redshift, is straightforward to integrate, and doesn’t require any changes to your Amazon Redshift data, schema, or how your users interact with data. Leave the rest of the tabs with their default settings and choose Save.

Data Warehouse 84

Data Warehouse Interactive Data Architecture Data Lake 84

DataKitchen

JANUARY 23, 2023

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more. Are problems with data tests?

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

MAY 1, 2023

that gathers data from many sources. Data Environment First off, the solutions you consider should be compatible with your current data architecture. We have outlined the requirements that most providers ask for: Data Sources Strategic Objective Use native connectivity optimized for the data source.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52