ProjectPro

JUNE 6, 2015

In an era of digital transformation of enterprises, there are several questions that have arisen- How can business intelligence provide real time insights? How can business intelligence scale and analyse the growing data heap? How can business intelligence meet changing business needs?

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

AltexSoft

SEPTEMBER 23, 2021

The main purpose of a DW is to enable analytics: It is designed to source raw historical data, apply transformations, and store it in a structured format. This type of storage is a standard part of any business intelligence (BI) system, an analytical interface where users can query data to make business decisions.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 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. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Knowledge Hut

JULY 26, 2023

It also has strong querying capabilities, including a large number of operators and indexes that allow for quick data retrieval and analysis. Database Software- Other NoSQL: NoSQL databases cover a variety of database software that differs from typical relational databases. Columnar Database (e.g.-

Database 52

Database NoSQL Telecommunication MongoDB 52

Knowledge Hut

JUNE 23, 2023

You must develop predictive models to help industries and businesses make data-driven decisions. Steps to Become a Data Engineer One excellent point is that you don’t need to enter the industry as a data engineer. Data warehousing to aggregate unstructured data collected from multiple sources.

Data Engineer 52

Data Engineer Data Engineering Engineering 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 Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

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

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Edureka

FEBRUARY 9, 2023

Organizations frequently use Hadoop to store and analyse big data from a variety of sources, including social media, internet of things (IoT) devices, and log files. Tableau: Tableau is a business intelligence and data visualization software that enables users to connect, visualize, and share data insights.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

AltexSoft

DECEMBER 23, 2022

One of the main reasons behind this is the need to timely process huge volumes of data in any format. As said, ETL and ELT are two approaches to moving and manipulating data from various sources for business intelligence. In ETL, all the transformations are done before the data is loaded into a destination system.

Process 52

Process Building Raw Data Data Lake 52

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

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. It follows a predefined schema and enforces data normalization and standardization.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Knowledge Hut

JULY 24, 2023

According to recent studies, the global database market will grow from USD 63.4 SQL is a powerful tool for managing and manipulating relational databases, and it continues to be widely used in the industry today. One of its most significant benefits is its ability to quickly process a vast amount of data.

Database 52

Database SQL Process NoSQL 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. Data warehouse. Traditional data warehouse platform architecture. Data lake architecture example. Business intelligence support.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Striim

SEPTEMBER 11, 2024

It typically includes large data repositories designed to handle varying types of data efficiently. Data Warehouses: These are optimized for storing structured data, often organized in relational databases.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 52

Big Data Hadoop Relational Database AWS 52

AltexSoft

MAY 14, 2021

And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. This data isn’t just about structured data that resides within relational databases as rows and columns. Data storage and processing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

OCTOBER 3, 2022

Bringing data together from heterogeneous enterprise sources and creating a unified view of that data is a popular application of knowledge graphs. This is especially useful for big enterprises when the sources are presented in different formats (CSV, XML, JSON, relational databases) and use different data schemas.

Relational Database 52

Relational Database Banking Media Computer Science 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

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

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 use cases. Just like display cases in a store.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

ProjectPro

JULY 27, 2021

Differentiate between relational and non-relational database management systems. Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language).

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

Knowledge Hut

SEPTEMBER 26, 2023

Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

AUGUST 26, 2015

1997 -The term “BIG DATA” was used for the first time- A paper on Visualization published by David Ellsworth and Michael Cox of NASA’s Ames Research Centre mentioned about the challenges in working with large unstructured data sets with the existing computing systems. Truskowski. zettabytes.

Big Data 40

Big Data Unstructured Data Hadoop NoSQL 40

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. Step 2- Internal Data transformation at LakeHouse.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

SEPTEMBER 21, 2023

These indices are specially designed data structures that map out the data for rapid searches, allowing for the retrieval of queries in milliseconds. As a result, Elasticsearch is exceptionally efficient in managing structured and unstructured data. Framework Programming The Good and the Bad of Node.js

Engineering 52

Engineering NoSQL Programming Language Java 52

AltexSoft

JULY 29, 2022

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

SEPTEMBER 6, 2023

They highlight competence in data management, a pivotal requirement in today's business landscape, making certified individuals a sought-after asset for employers aiming to efficiently handle, safeguard, and optimize data operations. You can begin by getting a beginner's certification to step into the database world.

Database 52

Database Certification MongoDB MySQL 52

ProjectPro

SEPTEMBER 11, 2015

For professionals from BI background, learning Hadoop is necessary because with data explosion it is becoming difficult for traditional databases to store unstructured data. Hadoop still has a long way to go when it comes to presenting clean and readable data solutions. Hadoop is not suitable for all kinds of data.

Hadoop 52

Hadoop Java BI Big Data 52

Knowledge Hut

DECEMBER 26, 2023

Let us first get a clear understanding of why Data Science is important. What is the need for Data Science? If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets.

Data Engineer 98

Data Engineer Data Engineering Engineering Pipeline-centric 98

U-Next

AUGUST 3, 2022

A high-ranking expert is known as a “Data Scientist” who works with big data and has the mathematics, economic, technical, analytic, and technological abilities necessary to cleanse, analyse and evaluate organised and unstructured data to help organisations make more informed decisions.

Data Science 52

Data Science Retail Computer Science Entertainment 52

Knowledge Hut

DECEMBER 26, 2023

Big Data: Concepts, Technology and Architecture For data scientists, engineers, and database managers, Big Data is the best book to learn big data. It belongs in the bookcases of business intelligence analysts as well because they have to make decisions based on a ton of data.

Big Data 75

Big Data Data Mining Business Intelligence Machine Learning 75