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

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data discoverability Unlike structured data, which is managed in well-defined rows and columns, unstructured data is stored as objects.

Data Governance 93

Data Governance Unstructured Data Metadata Data Lake 93

CIO Business Intelligence

OCTOBER 13, 2023

The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.

Data Architecture 127

Data Architecture Data Warehouse Statistics Visualization 127

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 133

IT Metadata Data-driven Data Governance 133

erwin

JUNE 26, 2020

If you’re serious about a data-driven strategy , you’re going to need a data catalog. Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner. Three Types of Metadata in a Data Catalog.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

AWS Big Data

DECEMBER 5, 2023

A combination of Amazon Redshift Spectrum and COPY commands are used to ingest the survey data stored as CSV files. For the files with unknown structures, AWS Glue crawlers are used to extract metadata and create table definitions in the Data Catalog. She helps customers architect data analytics solutions at scale on AWS.

Measurement 81

Measurement Dashboards Data Warehouse Analytics 81

CIO Business Intelligence

OCTOBER 26, 2023

The role is becoming increasingly important as organizations move to capitalize on the volumes of data they collect through business intelligence strategies. It’s a role that combines hard skills such as programming, data modeling, and statistics with soft skills such as communication, analytical thinking, and problem-solving.

Business Intelligence 125

Business Intelligence Data-driven Statistics Data Warehouse 125

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 52

AWS Big Data

MARCH 2, 2023

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

Data Lake 116

Data Lake Data Processing Metadata Snapshot 116

Cloudera

JULY 14, 2023

Depending on the size and usage patterns of the data, several different strategies could be pursued to achieve a successful migration. In this blog, I will describe a few strategies one could undertake for various use cases. The underlying data file locations will not change.

Snapshot 70

Snapshot Metadata Data Warehouse Optimization 70

AWS Big Data

MARCH 10, 2023

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. One important aspect to a successful data strategy for any organization is data governance.

Data Lake 100

Data Lake Sales Data Warehouse Snapshot 100

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

AWS Big Data

JANUARY 24, 2024

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. The output will give a count of the number of data and metadata files deleted.

Snapshot 98

Snapshot Data Lake Metadata Optimization 98

Ontotext

NOVEMBER 16, 2023

Data mesh is still in its infancy, and data personas and organizations are craving clarity and specificity. It is critical to be aware of the “why” and “what” and fully understand the role that knowledge graphs play when considering adopting a data mesh strategy. The debate on what constitutes a data mesh rages on.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Cloudera

NOVEMBER 12, 2020

Moving to a cloud-only based model allows for flexible provisioning, but the costs accrued for that strategy rapidly negate the advantage of flexibility. . Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs. How Burst to Cloud can solve your data center pressure.

Data Warehouse 101

Data Warehouse Reporting Risk Cost-Benefit 101

IBM Big Data Hub

JUNE 8, 2022

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. Integrating data across this hybrid ecosystem can be time consuming and expensive. The volume of data assets.

Data-driven 57

Data-driven Enterprise Data Governance Data Lake 57

FineReport

MAY 6, 2020

Menurut saya, data analyst nampaknya cuma menganalisis data bisnis dan saya tidak tahu bagaimana cara meningkatkan skill saya.” Ini karena dia tidak sepenuhnya menggali nilai dari analisis big data. Pengetahuan bisnis saja tidaklah cukup, yang lebih penting adalah untuk menguasai berbagai skill untuk menganalisis data.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

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. The analysts call this a data mesh or data fabric strategy.

Data Lake 105

Data Lake Recreation/Entertainment Metadata Data Warehouse 105

TDAN

APRIL 19, 2022

Introduction We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to harness their data wealth effectively.

Data Warehouse 72

Data Warehouse Enterprise IT Metadata 72

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. Creating a High-Quality Data Pipeline.

Data Governance 40

Data Governance Risk Metadata Management 40

Octopai

MARCH 24, 2020

Let’s start with automated tools that foster the seamless interaction of multiple metadata best practices, such as data discovery, data lineage and the use of a business glossary. Here is an overview of how automated metadata management makes your business intelligence smarter. How Can Smarter BI Impact Your Company?

Metadata 43

Metadata Cost-Benefit Business Intelligence ROI 43

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 111

Data Lake Analytics Dashboards Metrics 111

datapine

SEPTEMBER 29, 2022

Effective DQM is recognized as essential to any consistent data analysis, as the quality of data is crucial to derive actionable and – more importantly – accurate insights from your information. There are a lot of strategies that you can use to improve the quality of your information. 2 – Data profiling.

Data Quality 241

Data Quality Metrics Data-driven Management 241

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 Governance Data-driven 69

Alation

APRIL 14, 2023

Not to say that pragmatists alone are key to boardroom strategy sessions. In “The modern data stack is dead, long live the modern data stack!” the presenters elaborated on the common pain points of the cloud data warehouse today and predicted what it may look like in the future. Cloud costs are growing prohibitive.

Data Warehouse 52

Data Warehouse Digital Transformation Data-driven Metadata 52

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Andrew White

OCTOBER 22, 2021

Monetization/Link data to outcome (value pyramid) business value of data/business impact 20. D&A Strategy/infusing business with (overall) 16. Business Information Model/Arch compared to classic enterprise data model and how to relate it to catalogs and marketplaces and enterprise data models 13.

IT 52

IT Data Lake Strategy Data Science 52

CIO Business Intelligence

SEPTEMBER 6, 2022

As a result, Pimblett now runs the organization’s data warehouse, analytics, and business intelligence. Establishing a clear and unified approach to data. In a first test of the technology, he used Alation to catalog a subset of Very’s data held in an old Teradata database. We’re a Power BI shop,” he says. “I

IT 82

IT Forecasting Data Lake Enterprise 82

TDAN

MARCH 19, 2019

Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or Data Lakes indicate that changes are under way.

Management 84

Management Data Lake Data Warehouse Analytics 84

Alation

JANUARY 26, 2023

Data is a key asset for businesses in the modern world. Used correctly, it can improve internal operations, power marketing strategies, and much more. That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. This is why data also needs to be compliant.

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

AWS Big Data

FEBRUARY 24, 2023

An AWS Glue streaming job is connected to Kinesis Data Streams to process the data using the Iceberg connector. Athena uses the AWS Glue Data Catalog to store and retrieve table metadata for the Amazon S3 data in Iceberg format. To create your data warehouse or data lake, you must catalog this data.

Data Lake 82

Data Lake Metadata Testing Data Warehouse 82

Cloudera

FEBRUARY 11, 2019

Also, its ability to develop a product strategy that meets the market’s requirements, to comprehend overall market trends, and to influence or lead the market when necessary. All this while the platform serves as the core foundation providing metadata and governance capabilities across these workloads.

Management 43

Management Metadata Analytics Machine Learning 43

Cloudera

MAY 12, 2021

Use cases could include but are not limited to: workload analysis and replication, migrating or bursting to cloud, data warehouse optimization, and more. DATA FOR GOOD. SECURITY AND GOVERNANCE LEADERSHIP. A popular category last year, and no doubt this year too.

Digital Transformation 66

Digital Transformation Machine Learning Optimization Data Lake 66

AWS Big Data

JANUARY 31, 2023

Amazon Redshift is a fully-managed, petabyte-scale data warehouse service in the AWS Cloud. The data sharing feature allows data owners to group databases, tables, and views in an Amazon Redshift cluster and share it with other Amazon Redshift clusters within or across AWS accounts.

Data Lake 80

Data Lake Data Governance Data Architecture Machine Learning 80

Sisense

MAY 28, 2020

As enterprises demand data infrastructures that can meet this growth in real-time data — and ultimately assist with their product differentiation strategy — the pressure put on product teams is huge. Product teams are already having to manage the growing complexities that come with modern data environments.

Internet of Things 83

Internet of Things Metadata Data-driven Unstructured Data 83

erwin

FEBRUARY 7, 2019

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation. The volume and variety of data has snowballed, and so has its velocity. That’s time needed and better used for data analysis. Metadata-Driven Automation in the BFSI Industry.

Metadata 96

Metadata Insurance Data-driven Cost-Benefit 96

Jet Global

JUNE 5, 2019

Transforming your raw data into business insight via the process of data mining takes place over five steps: Extract, Transform, and Load (ETL): The first stage in data mining involves extracting data from one or many sources (such as those referenced above), transforming it into a standardized format, and loading it into the data warehouse.

Data mining 75

Data mining Business Intelligence OLAP Key Performance Indicator 75