AWS Big Data

AUGUST 15, 2024

Unifying these necessitates additional data processing, requiring each business unit to provision and maintain a separate data warehouse. This burdens business units focused solely on consuming the curated data for analysis and not concerned with data management tasks, cleansing, or comprehensive data processing.

Data Lake 122

Data Lake Data Warehouse Data Governance Publishing 122

Octopai

DECEMBER 12, 2021

What enables you to use all those gigabytes and terabytes of data you’ve collected? Metadata is the pertinent, practical details about data assets: what they are, what to use them for, what to use them with. Without metadata, data is just a heap of numbers and letters collecting dust. Where does metadata come from?

Metadata 98

Metadata Management Data Quality Data Governance 98

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

AWS Big Data

JANUARY 15, 2025

An extract, transform, and load (ETL) process using AWS Glue is triggered once a day to extract the required data and transform it into the required format and quality, following the data product principle of data mesh architectures. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 111

IoT Machine Learning Metadata Data-driven 111

DataKitchen

AUGUST 3, 2021

The past decades of enterprise data platform architectures can be summarized in 69 words. First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Secure and permissioned – data is protected from unauthorized users.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

CIO Business Intelligence

MAY 24, 2022

In order to help maintain data privacy while validating and standardizing data for use, the IDMC platform offers a Data Quality Accelerator for Crisis Response.

Finance 140

Finance Management Metadata Machine Learning 140

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

Octopai

FEBRUARY 12, 2020

Like any good puzzle, metadata management comes with a lot of complex variables. That’s why you need to use data dictionary tools, which can help organize your metadata into an archive that can be navigated with ease and from which you can derive good information to power informed decision-making. Why Have a Data Dictionary? #1

Metadata 72

Metadata Enterprise Management Data Warehouse 72

AWS Big Data

FEBRUARY 29, 2024

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. Implement data privacy policies. Implement data quality by data type and source.

Data Governance 124

Data Governance Unstructured Data Metadata Data Lake 124

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.

Data Architecture 111

Data Architecture Optimization Data Warehouse Metadata 111

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

Timo Elliott

JANUARY 6, 2022

The data you’ve collected and saved over the years isn’t free. If storage costs are escalating in a particular area, you may have found a good source of dark data. Analyze your metadata. If you’ve yet to implement data governance, this is another great reason to get moving quickly. Data sense-making.

IT 135

IT Metadata Data-driven Data Governance 135

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 173

Testing Data Quality Predictive Modeling Metrics 173

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

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

AWS Big Data

DECEMBER 4, 2024

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

Data Integration 115

Data Integration Data Lake Statistics Data-driven 115

AWS Big Data

DECEMBER 9, 2024

As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. Initialize the SparkSession with Iceberg settings.

Snapshot 64

Snapshot Data Warehouse Data Lake Data Quality 64

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. It’s a good idea to record metadata.

Metadata 130

Metadata Visualization Unstructured Data Data mining 130

AWS Big Data

MAY 7, 2024

It also makes it easier for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization to discover, use, and collaborate to derive data-driven insights. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 107

Metadata Data Lake Data Processing Data-driven 107

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

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

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 97

Analytics Data Warehouse Data Lake Metadata 97

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. Then, you transform this data into a concise format.

Data Strategy 99

Data Strategy Strategy Data Warehouse Prescriptive Analytics 99

Alation

OCTOBER 18, 2022

In the modern data stack, dbt is a key tool to make data ready for analysis. Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Conclusion.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

Smart Data Collective

OCTOBER 13, 2021

Data Virtualization can include web process automation tools and semantic tools that help easily and reliably extract information from the web, and combine it with corporate information, to produce immediate results. How does Data Virtualization manage data quality requirements?

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback.

Metadata 80

Metadata Data Warehouse Snapshot Machine Learning 80

erwin

MARCH 8, 2019

Therefore, the organization needed to catalog the data it acquires from suppliers, ensure its quality, classify it, and then sell it to customers. The company wanted to assemble the data in a data warehouse and then provide controlled access to it. This, among other safeguards, ensures data quality.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

CIO Business Intelligence

DECEMBER 21, 2022

Stout, for instance, explains how Schellman addresses integrating its customer relationship management (CRM) and financial data. “A A lot of business intelligence software pulls from a data warehouse where you load all the data tables that are the back end of the different software,” she says. “Or

IT 131

IT Business Intelligence Sales Key Performance Indicator 131

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 111

Enterprise Data Warehouse Snapshot Cost-Benefit 111

Cloudera

AUGUST 30, 2022

For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Forrester ).

Data Architecture 79

Data Architecture Data Lake Data Warehouse Metadata 79

Octopai

JULY 12, 2020

After all, how do you adjust this month’s operations based on last month’s data if it takes two weeks to finally receive the information you need? This is exactly how Octopai customer, Farm Credit Services of America (FCSA) , felt when their BI team needed to modernize their data warehouse.

Data Warehouse 84

Data Warehouse Data Quality Metadata Risk Management 84

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale.

Management 79

Management Metadata Data Architecture Data Lake 79

Cloudera

SEPTEMBER 9, 2022

analyst Sumit Pal, in “Exploring Lakehouse Architecture and Use Cases,” published January 11, 2022: “Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support AI, BI, ML, and data engineering on a single platform.” Iceberg handles massive data born in the cloud.

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

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

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Timo Elliott

DECEMBER 14, 2022

Most innovation platforms make you rip the data out of your existing applications and move it to some another environment—a data warehouse, or data lake, or data lake house or data cloud—before you can do any innovation. But that’s like ripping a tree out of the forest and trying to get it to grow elsewhere.

Data Lake 105

Data Lake Recreation/Entertainment Data Warehouse Metadata 105