Smart Data Collective

AUGUST 28, 2021

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Key Differences.

Data Lake 140

Data Lake Data Warehouse Unstructured Data Structured Data 140

AWS Big Data

APRIL 6, 2023

TBAC helps us categorize the data into a simple, broad level or a complex, more granular level using tags and then grant consumers access to those tags based on what group of data they need. Tag-based entitlements allow us to have a flexible and manageable entitlements system that solves our complex entitlements scenarios.

Data Warehouse 83

Data Warehouse Data Lake Management Data-driven 83

Data Virtualization

DECEMBER 8, 2022

Nowadays, we no longer use the term DD/DS, but “data catalog” or simply “metadata system”. The post Metadata, the Neglected Stepchild of IT appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information. It was written by L.

Metadata 69

Metadata IT Data Integration Publishing 69

CIO Business Intelligence

APRIL 25, 2024

Currently, a handful of startups offer “reverse” extract, transform, and load (ETL), in which they copy data from a customer’s data warehouse or data platform back into systems of engagement where business users do their work. It works in Salesforce just like any other native Salesforce data,” Carlson said.

Data Integration 85

Data Integration Data Lake Metadata Data Warehouse 85

Cloudera

SEPTEMBER 29, 2020

Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. . Cloudera Data Warehouse vs HDInsight.

Data Warehouse 116

Data Warehouse Metadata Data-driven Machine Learning 116

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

AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. ExampleCorp has multiple Redshift clusters.

Management 93

Management Data Warehouse Metrics Dashboards 93

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Introduction.

Data Warehouse 91

Data Warehouse Cost-Benefit Consulting Interactive 91

Cloudera

DECEMBER 11, 2020

In this blog post, we compare Cloudera Data Warehouse (CDW) on Cloudera Data Platform (CDP) using Apache Hive-LLAP to EMR 6.0 (also powered by Apache Hive-LLAP) on Amazon using the TPC-DS 2.9 Cloudera Data Warehouse vs EMR. Learn more about Cloudera Data Warehouse on CDP.

Data Warehouse 106

Data Warehouse Metadata Machine Learning Measurement 106

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. Download Now.

Metadata 54

Metadata Enterprise Management Data Warehouse 54

Ontotext

DECEMBER 15, 2022

How enterprises choose to manage their data so they can take the most advantage of it depends on what they want to achieve. But whatever their business goals, in order to turn their invisible data into a valuable asset, they need to understand what they have and to be able to efficiently find what they need. Enter metadata.

Metadata 52

Metadata Unstructured Data Structured Data Enterprise 52

CIO Business Intelligence

MAY 24, 2022

Just after launching a focused data management platform for retail customers in March, enterprise data management vendor Informatica has now released two more industry-specific versions of its Intelligent Data Management Cloud (IDMC) — one for financial services, and the other for health and life sciences.

Finance 132

Finance Management Metadata Data Quality 132

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

Data Virtualization

APRIL 21, 2022

Reading Time: 3 minutes First we had data warehouses, then came data lakes, and now the new kid on the block is the data lakehouse. But what is a data lakehouse and why should we develop one? In a way, the name describes what.

Data Lake 59

Data Lake Data Warehouse Data Integration Management 59

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

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 67

Data Warehouse Cost-Benefit Metadata Management 67

Octopai

JULY 18, 2019

Data Governance Starts With Metadata Management. Like all data governance, healthcare data governance requires tight management and control of metadata —the information about every database, table, column, data warehouse cube, and report element for which the organization is responsible.

Metadata 48

Metadata Management Data Governance Insurance 48

Ontotext

APRIL 10, 2024

Data ecosystems have become jungles and in spite of all the technology, data teams are struggling to create a modern data experience. In most enterprises data teams lack a data map and data asset inventory and are often unaware of data that exists across the organization, its associated profile, quality and associated metadata.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

IBM Big Data Hub

OCTOBER 16, 2023

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It can be used with both on-premise and multi-cloud environments.

Risk 75

Risk Modeling Management Metadata 75

AWS Big Data

APRIL 3, 2024

Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more. AWS provides integrations for various AWS services with Iceberg tables as well, including AWS Glue Data Catalog for tracking table metadata.

Data Lake 93

Data Lake Snapshot Metadata Data Architecture 93

AWS Big Data

FEBRUARY 29, 2024

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. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

Data Governance 93

Data Governance Unstructured Data Metadata Data Lake 93

datapine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

Data Quality 241

Data Quality Metrics Data-driven Management 241

CIO Business Intelligence

OCTOBER 13, 2023

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. Data architects are frequently part of a data science team and tasked with leading data system projects.

Data Architecture 126

Data Architecture Data Warehouse Statistics Visualization 126

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 101

Data Lake Management Metrics Data Warehouse 101

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 99

Data Warehouse Data Lake Analytics Machine Learning 99

AWS Big Data

OCTOBER 4, 2023

Common pain points of data management and governance: Discovery of data , especially data distributed across accounts and regions – Finding the data to use for analysis is challenging because organizations often have petabytes of data spread across tens or even thousands of data sources.

Metadata 98

Metadata Data Lake Publishing Data Governance 98

Octopai

APRIL 8, 2019

Their activities can have a direct impact on the ability of the profit center to increase revenue by providing the right information at the right time to management to make the right decisions. There are two primary reasons for this: Manual data discovery and manual data lineage. Money Loser #1: Manual Data Discovery.

Metadata 44

Metadata Dashboards Data Warehouse Visualization 44

erwin

JUNE 26, 2020

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer. It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., Three Types of Metadata in a Data Catalog. Technical Metadata.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

Ontotext

AUGUST 22, 2023

Poor data management, data silos, and a lack of a common understanding across systems and/or teams are the root cause that prohibits an organization from scaling the business in a dynamic environment. Beyond that, and without a way to visualize, connect, and utilize the data, it’s still just a bunch of random information.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse. These costs impede the adoption of cloud-native data warehouses.

Data Warehouse 98

Data Warehouse Metadata Cost-Benefit Optimization 98

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 82

Enterprise Data Warehouse Snapshot Cost-Benefit 82

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map.

Data Lake 91

Data Lake Data Warehouse Metadata Data Architecture 91

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Conclusion.

Management 52

Management Data Quality Metadata Testing 52

Jen Stirrup

MARCH 29, 2023

Data as a product as well as a foundation The data foundation is critical for all types of reporting and analytics. Data needs to be respected in the same way as building a product. There’s a new trend for people to talk about data products, bringing a product management mindset to the production of data assets.

Metadata 120

Metadata Data Warehouse Data Integration Business Intelligence 120

AWS Big Data

JULY 27, 2023

Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, easy, and secure analytics at scale. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the widely used cloud data warehouse.

Data Warehouse 82

Data Warehouse Analytics Metadata Dashboards 82

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

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. The past decades of enterprise data platform architectures can be summarized in 69 words. And you guessed it, managed by a specialized team drowning in technical debt.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246