Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. Make sure that the value of data increases over time.

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. Several factors are driving the adoption of knowledge graphs.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Alation

JUNE 14, 2021

Intelligence automatically surfaces clues in the data to remove the manual effort otherwise required for discovery; intelligence can also flag sensitive data within the huge volume, variety, and veracity of data facing the modern enterprise. These are essential to enabling a more rapid process of sensitive data discovery.

Data Governance 97

Data Governance Recreation/Entertainment Data Lake Digital Transformation 97

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

Alation

OCTOBER 21, 2021

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. Many organizations have built a data lake to solve their data storage, access, and utilization challenges.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

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. Towards Data Science ). Forrester ).

Data Architecture 76

Data Architecture Data Lake Metadata Data Warehouse 76

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. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Enhanced multi-function analytics.

Metadata 76

Metadata Data Warehouse Snapshot Data Quality 76

Ontotext

FEBRUARY 12, 2024

It proposes a technological, architectural, and organizational approach to solving data management problems by breaking up the monolithic data platform and de-centralizing data management across different domain teams and services. Some examples of data products are data sets, tables, machine learning models, and APIs.

IT 64

IT Metadata Data Quality Data Lake 64

IBM Big Data Hub

APRIL 27, 2022

Since its uniquely metadata-driven, the abstraction layer of a data fabric makes it easier to model, integrate and query any data sources, build data pipelines, and integrate data in real-time. This improves data engineering productivity and time-to-value for data consumers. What’s a data mesh?

Management 83

Management Metadata Data Architecture Data Lake 83

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.” According to Gartner, Inc.

Metadata 97

Metadata Machine Learning Unstructured Data Data Lake 97

IBM Big Data Hub

DECEMBER 7, 2023

Here’s an overview of the key characteristics: AI-powered analytics : Integration of AI and machine learning capabilities into OLAP engines will enable real-time insights, predictive analytics and anomaly detection, providing businesses with actionable insights to drive informed decisions.

OLAP 60

OLAP Slice and Dice Cost-Benefit Data Warehouse 60

Cloudera

SEPTEMBER 6, 2022

Previously, there were three types of data structures in telco: . Entity data sets — i.e. marketing data lakes . It is an edge-to-AI suite of capabilities, including edge analytics, data staging, data quality control, data visualization tools, and machine learning.

Data Architecture 83

Data Architecture Cost-Benefit Digital Transformation Business Driver 83

Alation

AUGUST 11, 2022

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. So we have to be very careful about giving the domains the right and authority to fix data quality. Let’s take data privacy as an example.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 79

Cost-Benefit Metadata Optimization Management 79

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

Cost-Benefit 79

Cost-Benefit Metadata Data Governance Modeling 79

Alation

MARCH 21, 2023

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So Subscribe to Alation's Blog Get the latest data cataloging news and trends in your inbox.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Domino Data Lab

JUNE 15, 2021

At a certain point, as the demand keeps growing, the data volumes rapidly increase. Data is no longer stored in CSV files, but in a dedicated, purpose built data lake / data warehouse. In this blog post we’ll focus on the UDF capabilities provided by the two platforms. Why Snowflake UDFs.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Cloudera

JANUARY 22, 2019

Forrester describes Big Data Fabric as, “A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, data lakes, in-memory, and NoSQL.”.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

datapine

AUGUST 29, 2022

James Warren, on the other part, is a successful analytics architect with a background in machine learning and scientific computing. 5) Data Analytics Made Accessible, by Dr. Anil Maheshwari. Best for : the new intern who has no idea what data science even means.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Smarten

AUGUST 4, 2023

If your team has easy-to-use tools and features, you are much more likely to experience the user adoption you want and to improve data literacy and data democratization across the organization. Look for a solution with no requirement for SQL skills or the need for manual skills in data extraction, transformation, and loading (ETL).

Data Lake 52

Data Lake Machine Learning Data Integration Optimization 52

Alation

AUGUST 4, 2022

Many CIOs argue the rise of big data pushed people to use data more proactively for business decision-making. Big data got“ more leaders and people in the organization to use data, analytics, and machine learning in their decision making,” says former CIO Isaac Sacolick. Big data can grow too big fast.

Big Data 95

Big Data Digital Transformation Data Lake Data-driven 95

Ontotext

AUGUST 4, 2023

This logical data architecture is designed to help organizations deal with growing volumes of data, spanning data silos with seamless connectivity and a knowledge layer. Using metadata, machine learning (ML), and automation, a data fabric provides a unified view of enterprise data across data formats and locations.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

erwin

DECEMBER 13, 2018

As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.), organizations will need to get a better handle on data quality and focus on data management processes and practices. To stay up to date, click here to subscribe.

Data Governance 91

Data Governance IoT Internet of Things Data-driven 91

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

IBM Big Data Hub

JULY 27, 2023

Data quality strongly impacts the quality and usefulness of content produced by an AI model, underscoring the significance of addressing data challenges. It provides the combination of data lake flexibility and data warehouse performance to help to scale AI.

Management 57

Management Enterprise Modeling Data Quality 57

Alation

JANUARY 27, 2022

What Are the Top Data Challenges to Analytics? The proliferation of data sources means there is an increase in data volume that must be analyzed. Large volumes of data have led to the development of data lakes , data warehouses, and data management systems. Establishes Trust in Data.

Data Governance 52

Data Governance Analytics Cost-Benefit Data-driven 52