Knowledge Hut

JANUARY 30, 2024

Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Cloudera

DECEMBER 11, 2020

It established a data governance framework within its enterprise data lake. Powered and supported by Cloudera, this framework brings together disparate data sources, combining internal data with public data, and structured data with unstructured data.

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Monte Carlo

JULY 13, 2023

In this article, we will explore how automation can help data engineering teams overcome these challenges and provide a more seamless and effective data management process. Automated processes scan all data stores and discover data, including semi-structured data such as JSON on a continual basis.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Monte Carlo

SEPTEMBER 27, 2023

Testing your data early and often helps detect common quality issues before they have the chance to impact downstream data consumers or products. Data governance can also include data quality standards that define benchmarks for accuracy, completeness, and consistency.

Data Governance 52

Data Governance Government Data Data Pipeline 52

AltexSoft

AUGUST 29, 2023

If your organization fits into one of these categories and you’re considering implementing advanced data management and analytics solutions, keep reading to learn how data lakes work and how they can benefit your business. Data sources can be broadly classified into three categories. Structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Precisely

SEPTEMBER 6, 2023

.” Poor data quality impedes the success of data programs, hampers data integration efforts, limits data integrity causing big data governance challenges. To truly succeed in an increasingly data-driven world, organizations need data governance. The results are clear.

Data Governance 52

Data Governance Big Data Government Unstructured Data 52

Knowledge Hut

JULY 26, 2023

These datasets typically involve high volume, velocity, variety, and veracity, which are often referred to as the 4 v's of Big Data: Volume: Volume refers to the vast amount of data generated and collected from various sources. Managing and analyzing such large volumes of data requires specialized tools and technologies.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

AltexSoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. What is data governance? billion in 2020 to $5.28

Data Governance 52

Data Governance Government Healthcare Programming 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering Data Engineering involves various operations like creating data pipelines, strategizing data management plans, processing data, and many more. Data Management Data management is an integral part of high-scale organizations.

Data Science 52

Data Science Scala Programming Language R (Programming) 52

Monte Carlo

APRIL 24, 2023

If not paired with Glue, or another metastore/catalog solution, S3 will also lack some of the metadata structure required for more advanced data management tasks. AWS is one of the most popular data lake vendors. The added structure and governance from Dataplex makes BigLake an intriguing data lakehouse option as well.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. Potential downsides of data lakes include governance and integration challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. Potential downsides of data lakes include governance and integration challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. Potential downsides of data lakes include governance and integration challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

Cloudera

MAY 14, 2024

To attain that level of data quality, a majority of business and IT leaders have opted to take a hybrid approach to data management, moving data between cloud, on-premises -or a combination of the two – to where they can best use it for analytics or feeding AI models. What do we mean by ‘true’ hybrid?

Cloud 84

Cloud Data Governance Unstructured Data Data Architecture 84

Monte Carlo

AUGUST 31, 2023

We’ll take a closer look at variables that can impact your data next. Migration to the cloud Twenty years ago, your data warehouse (a place to transform and store structured data) probably would have lived in an office basement, not on AWS or Azure. Rise of the Data Lakehouse Data warehouse or data lake?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

APRIL 25, 2024

Data management The process of obtaining, storing, and using data in a cost-effective, effective, and secure way is known as data management. As businesses increasingly rely on intangible assets to create value, an efficient data management strategy is more important than ever.

Big Data 52

Big Data Data Analytics Manufacturing Retail 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

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. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 84

Engineering Raw Data Data Science Scala 84

Ascend.io

JULY 19, 2023

Process: Building Robust Systems from Data Governance to Product Publishing Just as a well-oiled machine needs precise processes, a successful data mesh needs to establish and adhere to processes regarding data governance, data curation, and data product publishing.

Data Governance 40

Data Governance Government Metadata Data 40

Knowledge Hut

JULY 24, 2023

Disruptive Database Technologies All existing and upcoming businesses are adopting innovative ways of handling data. With these technologies, businesses and organizations enhance their data management procedures, upgrade their knowledge, and make better decisions using data. Disruptive database technologies are on them.

Database 52

Database NoSQL Management Relational Database 52

Knowledge Hut

SEPTEMBER 26, 2023

To understand further, let us look in detail at the advanced power BI skills required to prepare data and transform it into the right formats. Data Cleansing: Cleaning the data to make it error-free and valid is the most basic and essential data management skill you must have. The certification cost is $165 USD.

BI 52

BI Business Intelligence Raw Data Data Analysis 52

ProjectPro

JANUARY 27, 2023

If you're wondering how the ETL process can drive your company to a new era of success, this blog will help you discover what use cases of ETL make it a critical component in many data management and analytic systems. However, the vast volume of data will overwhelm you if you start looking at historical trends.

BI 52

BI ETL Tools Retail Healthcare 52

DareData

JANUARY 30, 2023

AWS Glue: A fully managed data orchestrator service offered by Amazon Web Services (AWS). Talend Data Fabric: A comprehensive data management platform that includes a range of tools for data integration, data quality, and data governance.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Monte Carlo

AUGUST 25, 2023

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

APRIL 1, 2021

Data Catalogs Can Drown in a Data Lake Although exceptionally flexible and scalable, data lakes lack the organization necessary to facilitate proper metadata management and data governance. Data discovery tools and platforms can help. Image courtesy of Adrian on Unsplash. Image courtesy of Barr Moses.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. Data warehouse.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

AUGUST 22, 2022

Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes. With these challenges in enterprise data management, there has to be an approach to overcoming them, right? The solution is called a data fabric.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

AltexSoft

SEPTEMBER 23, 2021

The result of experimentation supplies downstream applications with prepared data. A data hub serves as a gateway to dispense the required data. So the use of unstructured or semi-structured data is also available in a data hub, since a data lake can be a part of it. Data hub architecture.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Cloudera

OCTOBER 30, 2018

The abundant growth of data, maturation of machine algorithms, and future regulatory compliance demands from the European Union’s General Data Protection Regulation (GDPR) will shift the landscape for creating a single source of the truth for customer data. Additionally, they only provide one piece of the puzzle.

Unstructured Data 42

Unstructured Data Data Lake Algorithm Machine Learning 42

AltexSoft

OCTOBER 8, 2021

The bad news is, integrating data can become a tedious task, especially when done manually. Luckily, there are various data integration tools that support automation and provide a unified data view for more efficient data management. Data integration process. IBM DataStage Designer interface. Source: G2.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

JANUARY 31, 2022

Several organizations can quickly transform, integrate, and analyze their data with Snowflake's Data Cloud. They can also design and run data apps and securely share, gather, and commercialize real-time data. Each node in the cluster keeps a piece of the entire data set locally, like shared-nothing systems.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

AltexSoft

JULY 29, 2022

A cloud-native data platform, Snowflake comprises a data warehouse suitable for massive sets of structured and semi-structured data, a powerful SQL query engine, and a set of additional services — such as authentication, access control, metadata and infrastructure management, and more.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. It's an open-source database and data management framework.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Cloudera

OCTOBER 7, 2021

The concept of the data mesh architecture is not entirely new; Its conceptual origins are rooted in the microservices architecture, its design principles (i.e., need to integrate multiple “point solutions” used in a data ecosystem) and organization reasons (e.g., difficulty to achieve cross-organizational governance model).

Architecture 126

Architecture Metadata Government Kafka 126

Precisely

JANUARY 23, 2024

Data Governance Goes Mainstream To get the most from data analytics initiatives, organizations must proactively work to build data integrity. Doing so requires a sound data governance framework. As such, data governance is a key factor in determining how well organizations achieve compliance and trust.

Technology 69

Technology Data Governance Government Data Integration 69

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. Data storage component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59