Knowledge Hut

DECEMBER 29, 2023

Big Data is a part of this umbrella term, which encompasses Data Warehousing and Business Intelligence as well. A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. They construct pipelines to collect and transform data from many sources.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

AltexSoft

SEPTEMBER 23, 2021

A data hub is a central mediation point between various data sources and data consumers. It’s not a single technology, but rather an architectural approach that unites storages, data integration and orchestration tools. Data lake vs data hub. Data hub architecture.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Edureka

AUGUST 26, 2024

How to Become a Business Intelligence Manager? Job profiles also disclose the abilities required to succeed in this industry and become an authority in data analytics, business intelligence, and data visualization. Additionally, they should have a solid grasp of the Microsoft business intelligence stack.

BI 52

BI Business Intelligence Consulting Certification 52

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. While all three are about data acquisition, they have distinct differences.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. The data lakehouse’s semantic layer also helps to simplify and open data access in an organization.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. The data lakehouse’s semantic layer also helps to simplify and open data access in an organization.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Striim

OCTOBER 11, 2024

Change Data Capture (CDC) plays a key role here by capturing and streaming only the changes (inserts, updates, deletes) in real time, ensuring efficient data handling and up-to-date information across systems. As a result, stream processing makes real-time business intelligence feasible.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Striim

SEPTEMBER 11, 2024

They support complex querying and analytical processing, making them ideal for business intelligence and reporting. Data warehouses offer high performance and scalability, enabling organizations to manage large volumes of structured data efficiently.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

SEPTEMBER 30, 2014

While the initial era of ETL ignited enough sparks and got everyone to sit up, take notice and applaud its capabilities, its usability in the era of Big Data is increasingly coming under the scanner as the CIOs start taking note of its limitations. Industry experts place a great emphasis on individuals to learn Hadoop.

Hadoop 52

Hadoop ETL Tools Unstructured Data ETL System 52

RandomTrees

SEPTEMBER 27, 2024

Microsoft Fabric architecture: The core components of the Microsoft Fabric Seven workloads are part of the Microsoft Fabric architecture, and they operate on top of One Lake, the storage layer that eventually pulls data from Google Cloud Platform as well as Microsoft platforms and Amazon S3.

Database-centric 52

Database-centric Pipeline-centric IT BI 52

Snowflake

APRIL 5, 2023

With Snowflake’s support for multiple data models such as dimensional data modeling and Data Vault, as well as support for a variety of data types including semi-structured and unstructured data, organizations can accommodate a variety of sources to support their different business use cases.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

AltexSoft

MAY 14, 2021

It’s worth noting though that data collection commonly happens in real-time or near real-time to ensure immediate processing. With the ETL approach, data transformation happens before it gets to a target repository like a data warehouse, whereas ELT makes it possible to transform data after it’s loaded into a target system.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Monte Carlo

AUGUST 25, 2023

With pre-built functionalities and robust SQL support, data warehouses are tailor-made to enable swift, actionable querying for data analytics teams working primarily with structured data. This is particularly useful to data scientists and engineers as it provides more control over their calculations. Or maybe both.)

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Precisely

OCTOBER 10, 2024

Then add self-service business intelligence tools that are accessible to virtually anyone. Combine that with the advent of location-aware mobile devices, IoT sensors, digital marketing automation, and ever-increasing volumes of unstructured data, there is so much more information available to be analyzed.

Data Governance 52

Data Governance Government Data Unstructured Data 52

Edureka

AUGUST 2, 2023

Data processing analysts are experts in data who have a special combination of technical abilities and subject-matter expertise. They are essential to the data lifecycle because they take unstructured data and turn it into something that can be used.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Databand.ai

JULY 11, 2023

Data quality platforms can be standalone solutions or integrated into broader data management ecosystems, such as data integration, business intelligence (BI), or data analytics tools. In this article: Why Do You Need a Data Quality Platform?

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Knowledge Hut

JUNE 20, 2023

They transform unstructured data into scalable models for data science. Data Engineer vs Machine Learning Engineer: Responsibilities Data Engineer Responsibilities: Analyze and organize unstructured data Create data systems and pipelines.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. What is ELT? ELT vs ETL.

Process 52

Process Building Raw Data Data Lake 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. This way, Delta Lake brings warehouse features to cloud object storage — an architecture for handling large amounts of unstructured data in the cloud.

Scala 64

Scala Data Lake BI Machine Learning 64

AltexSoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Data mart implementation steps. Data mart use cases.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

ProjectPro

JANUARY 27, 2023

Top ETL Business Use Cases for Streamlining Data Management Data Quality - ETL tools can be used for data cleansing, validation, enriching, and standardization before loading the data into a destination like a data lake or data warehouse.

BI 52

BI ETL Tools Retail Healthcare 52

AltexSoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Knowledge Hut

JANUARY 30, 2024

Structured Data: Structured data sources, such as databases and spreadsheets, often require extraction to consolidate, transform, and make them suitable for analysis. Unstructured Data: Unstructured data, like free-form text, can be challenging to work with but holds valuable insights.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Monitoring: It is a component that ensures data integrity.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

MARCH 27, 2024

An organization can make informed decisions based on a big data analytics platform, which works by uncovering patterns, correlations, customer preferences and market trends hidden in the data. Technologies and techniques for data analytics enable organizations to gather new information and analyze data sets on a broad scale.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

Knowledge Hut

JULY 26, 2023

SQL (Structured Query Language) is a standard language for interacting with relational databases, allowing users to conduct operations such as data creation, modification, and querying. To ensure data consistency and reliability, the ACID (Atomicity, Consistency, Isolation, and Durability) properties are maintained.

Database 52

Database NoSQL MongoDB Telecommunication 52

Striim

AUGUST 22, 2024

More often than not, you need a data pipeline that begins with data integration and then enables you to do several things to the data in-flight before delivery to the target. Therefore, another essential component for real-time data analytics is the infrastructure to handle real-time event processing.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

Rockset

JULY 29, 2022

Data warehouses are popular because they help break down data silos and ensure data consistency. You can aggregate and analyze relevant data from multiple sources without worrying about inconsistent and inaccessible data. Additionally, data warehouses are great at offering historical intelligence.

Data Analytics 52

Data Analytics Data Warehouse Datasets Cloud 52

Cloudera

MAY 16, 2018

This example combines three types of unrelated data: Legal entity data: Two companies with completely unrelated business lines (coffee and waste management) merged together; Unstructured data: Fraudulent promotion campaigns took place through press releases and a fake stock-picking robot.

Data Lake 42

Data Lake Electronics Media Unstructured Data 42

ProjectPro

SEPTEMBER 26, 2021

Automated tools are developed as part of the Big Data technology to handle the massive volumes of varied data sets. Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. Similarly, advanced programming skills in R or Python give an edge for the role.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Knowledge Hut

JULY 4, 2023

Role and Responsibility of Data Scientist Selecting features, creating and optimizing classifiers with machine learning tools Preprocessing structured and unstructured data Improving data gathering techniques to include all important information for the development of analytic systems Data processing, cleansing, and validation to ensure data integrity (..)

Data Analytics 52

Data Analytics Data Architect Transportation Data Science 52

Knowledge Hut

DECEMBER 27, 2023

Many business owners and professionals are interested in harnessing the power locked in Big Data using Hadoop often pursue Big Data and Hadoop Training. What is Big Data? Big data is often denoted as three V’s: Volume, Variety and Velocity. Needs improvement in data handling capacity. Pricing : Free of cost.

Big Data Tools 59

Big Data Tools Big Data Hadoop Database-centric 59

Knowledge Hut

APRIL 20, 2023

It employs sophisticated methods to safeguard data confidentiality, preserve data integrity and authenticity, and ensure timely data availability. Deals with Statistical and computational approaches to extract knowledge and insights from structured and unstructured data.

Data Science 52

Data Science Computer Science Healthcare Recruitment 52

ProjectPro

MAY 20, 2015

Several big data companies are looking to tame the zettabyte’s of BIG big data with analytics solutions that will help their customers turn it all in meaningful insights. Our customers have sales people and use software, but they cannot step into the traditional way of buying business intelligence.

Big Data 40

Big Data Hadoop Business Analyst Data Analytics 40