Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

Data Warehouse 69

Data Warehouse Cost-Benefit Data-driven Data Governance 69

AWS Big Data

JUNE 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 88

Data Quality Data-driven Data Lake Metrics 88

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

AWS Big Data

JANUARY 15, 2025

cycle_end";') con.close() With this, as the data lands in the curated data lake (Amazon S3 in parquet format) in the producer account, the data science and AI teams gain instant access to the source data eliminating traditional delays in the data availability.

IoT 105

IoT Machine Learning Metadata Data-driven 105

IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

Data Quality 105

Data Quality Data Warehouse Data-driven Reporting 105

DataKitchen

JANUARY 19, 2022

The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. DataKitchen acts as a process hub that unifies tools and pipelines across teams, tools and data centers. Stronger Together.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

Data Governance 115

Data Governance Unstructured Data Metadata Data Lake 115

erwin

FEBRUARY 19, 2021

It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization. Better data quality. It harvests metadata from various data sources and maps any data element from source to target and harmonize data integration across platforms.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

AWS Big Data

SEPTEMBER 11, 2024

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections. 2 GB into the landing zone daily.

Data Architecture 101

Data Architecture Optimization Data Warehouse Metadata 101

BI-Survey

FEBRUARY 17, 2020

From operational systems to support “smart processes”, to the data warehouse for enterprise management, to exploring new use cases through advanced analytics : all of these environments incorporate disparate systems, each containing data fragments optimized for their own specific task. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

Smart Data Collective

OCTOBER 13, 2021

Agile BI and Reporting, Single Customer View, Data Services, Web and Cloud Computing Integration are scenarios where Data Virtualization offers feasible and more efficient alternatives to traditional solutions. Does Data Virtualization support web data integration?

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

CIO Business Intelligence

MARCH 22, 2022

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Set up unified data governance rules and processes.

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

CIO Business Intelligence

NOVEMBER 29, 2022

Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. Layering technology on the overall data architecture introduces more complexity. For data warehouses, it can be a wide column analytical table.

Data Architecture 111

Data Architecture Data Integration IoT Data-driven 111

AWS Big Data

DECEMBER 21, 2023

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. OpenSearch Service is used for multiple purposes, such as observability, search analytics, consolidation, cost savings, compliance, and integration.

Analytics 110

Analytics IT Data Lake Visualization 110

CIO Business Intelligence

FEBRUARY 16, 2023

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

Optimization 97

Optimization B2B Data Quality Sales 97

IBM Big Data Hub

APRIL 27, 2022

The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. Read: The first capability of a data fabric is a semantic knowledge data catalog, but what are the other 5 core capabilities of a data fabric? What’s a data mesh?

Management 79

Management Metadata Data Architecture Data Lake 79

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 82

Data Strategy Strategy Data Warehouse Prescriptive Analytics 82

BI-Survey

APRIL 27, 2023

While most continue to struggle with data quality issues and cumbersome manual processes, best-in-class companies are making improvements with commercial automation tools. The data vault has strong adherents among best-in-class companies, even though its usage lags the alternative approaches of third-normal-form and star schema.

Modeling 52

Modeling Data Warehouse Data Quality Business Driver 52

erwin

DECEMBER 12, 2019

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

Data Virtualization

JANUARY 21, 2025

The post OReilly Releases First Chapters of a New Book about Logical Data Management appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. Gartner predicts that by the end of this year, 30%.

Management 52

Management Data Integration Technology Data Warehouse 52

Andrew White

DECEMBER 4, 2019

I argued that one vendors’ book on data quality was really about data governance; I argued that another vendors’ marketing message was totally upside down; and I argued that some approaches to achieving single source of truth were different from traditional approaches. See Salesforce acquisition of Tableau – What does it mean?

Digital Transformation 64

Digital Transformation Data Quality Data Integration Data Warehouse 64

Jet Global

MAY 1, 2020

We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. The mechanical solution is to build a data warehouse. Data quality issues. Here’s the ugly truth: Everybody has a data quality problem. To us that equals one thing, and that’s risk.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

Wayne Yaddow

FEBRUARY 18, 2025

While transformations edit or restructure data to meet business objectives (such as aggregating sales data, enhancing customer information, or standardizing addresses), conversions typically deal with changing data formats, such as from CSV to JSON or string to integertypes.

Testing 52

Testing Data Transformation Data-driven Data Quality 52

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Ontotext

APRIL 10, 2024

Bad data tax is rampant in most organizations. Currently, every organization is blindly chasing the GenAI race, often forgetting that data quality and semantics is one of the fundamentals to achieving AI success. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Wayne Yaddow

MARCH 14, 2025

Users can apply built-in schema tests (such as not null, unique, or accepted values) or define custom SQL-based validation rules to enforce data integrity. dbt Core allows for data freshness monitoring and timeliness assessments, ensuring tables are updated within anticipated intervals in addition to standard schema validations.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

Cloudera

APRIL 14, 2021

Precisely Data Integration, Change Data Capture and Data Quality tools support CDP Public Cloud as well as CDP Private Cloud. Precisely Data Integration, Change Data Capture and Data Quality tools support CDP Public Cloud as well as CDP Private Cloud.

Data Warehouse 84

Data Warehouse Data Processing Machine Learning Data Quality 84

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

Cloudera

MAY 16, 2019

The conference provides a useful opportunity to reflect on the rapid evolution we’ve seen in the Data Integration and Management space, much of it driven by the innovations that Cloudera and the open source community have been delivering. The traditional Data Warehouse ETL process has splintered into many smaller components.

Analytics 40

Analytics Data Warehouse Data-driven Data Integration 40

AWS Big Data

JULY 12, 2023

Introduction to Amazon Redshift Amazon Redshift is a fast, fully-managed, self-learning, self-tuning, petabyte-scale, ANSI-SQL compatible, and secure cloud data warehouse. Thousands of customers use Amazon Redshift to analyze exabytes of data and run complex analytical queries.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

erwin

DECEMBER 12, 2019

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52