Cloudera

DECEMBER 20, 2021

But balancing a strong layer of security and governance with easy access to data for all users is no easy task. Another option — a more rewarding one — is to include centralized data management, security, and governance into data projects from the start. You can become a data hero too.

Government 89

Government Data Security Banking Metadata 89

Precisely

SEPTEMBER 11, 2023

By 2025, 80% of mainstream data quality vendors will expand their product capabilities to provide greater data insights by discovering patterns, trends, data relationships, and error resolution. Context-based bots expedite information retrieval from documentation, knowledge bases, or metadata.

Metadata 52

Metadata Data Integration Datasets Data Analysis Tools 52

Towards Data Science

JULY 24, 2023

A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section). Why are these things related, and more importantly, why should data leaders care?

Metadata 95

Metadata Data Warehouse Data Lake Data Science 95

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How Apache Iceberg tables structure metadata. Without a central query log, your team can run the risk of data loss and lack of data governance.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

JULY 18, 2023

A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section). Why are these things related, and more importantly, why should data leaders care?

Metadata 52

Metadata Data Warehouse Data Lake Government 52

Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

MARCH 14, 2023

This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Cloudera

FEBRUARY 16, 2022

Within the CML data service, model lineage is managed and tracked at a project level by the SDX. SDX provides open metadata management and governance across each deployed environment by allowing organisations to catalogue, classify as well as control access to and manage all data assets.

Machine Learning 83

Machine Learning Algorithm Government Metadata 83

Monte Carlo

JULY 18, 2023

A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section). Why are these things related, and more importantly, why should data leaders care?

Metadata 40

Metadata Data Warehouse Data Lake Government 40

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. ML workflow, ubr.to/3EJHjvm

Engineering 83

Engineering Raw Data Data Science Scala 83

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

AltexSoft

MAY 12, 2023

Unstructured data refers to information that lacks a predefined format or organization. In contrast, big data refers to large volumes of structured and unstructured data that are challenging to process, store, and analyze using traditional data management tools. Invest in data governance.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

phData: Data Engineering

AUGUST 4, 2023

Database Storage The Snowflake architecture’s database storage layer organizes data into multiple tiny partitions, which are compressed and optimized internally. Snowflake stores and manages data in the cloud using a shared disk approach, which simplifies data management.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Ascend.io

MARCH 14, 2023

Simply put, a data mesh is a platform architecture — a philosophy of sorts — that separates data into domains and defines the responsibilities of each. It provides a more distributed, decentralized, and resilient approach to data management. appeared first on Ascend.io.

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

Simply put, a data mesh is a platform architecture — a philosophy of sorts — that separates data into domains and defines the responsibilities of each. It provides a more distributed, decentralized, and resilient approach to data management. appeared first on Ascend.io.

Government 52

Government Architecture Data Lake Data 52

AltexSoft

JULY 29, 2022

A master node called NameNode maintains metadata with critical information, controls user access to the data blocks, makes decisions on replications, and manages slaves. Instruments like Apache ZooKeeper and Apache Oozie help better coordinate operations, schedule jobs, and track metadata across a Hadoop cluster.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

NOVEMBER 15, 2021

Apache Zeppelin Source: Github Apache Zeppelin is a multi-purpose notebook that supports Data Ingestion, Data Discovery, Data Analytics , Data Visualization , and Data Collaboration. It's an open-source database and data management framework. To contribute to this project, hop onto: [link] 19.DataHub

Big Data 52

Big Data Project Metadata Programming Language 52

phData: Data Engineering

JULY 18, 2023

To truly understand its potential, we need to explore the benefits it brings, particularly when transitioning from traditional data management structures. Why Migrate to a Modern Data Stack? Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52