Knowledge Hut

APRIL 25, 2024

Compute: Through the method of computing, or data processing, is an important aspect of Information Technology. It helps in storing the data in the CPU. Data Storage: The place where the information is stated somewhere safe without directly being processed. It is looked after by the Database Management System (DBMS).

Technology 52

Technology Recruitment Media Cloud Computing 52

Knowledge Hut

APRIL 25, 2023

With Power BI, data engineers can easily create interactive reports and dashboards that can be accessed from anywhere, on any device. Key features: Robust data visualization capabilities Seamless integration with Microsoft tools Easy-to-use interface 2. It is one of the most liked data engineering tools of the present day.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 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

Knowledge Hut

DECEMBER 13, 2023

Change is a constant, whether it be in the form of new businesses, products, processes, or approaches. Big Data startups compete for market share with the blue-chip giants that dominate the business intelligence software market. The top Data Analytics companies to take into account are listed below.

Big Data 52

Big Data Consulting Hadoop Amazon Web Services 52

Edureka

AUGUST 2, 2023

Data integration and transformation: Before analysis, data must frequently be translated into a standard format. Data processing analysts harmonise many data sources for integration into a single data repository by converting the data into a standardised structure.

Data Process 52

Data Process Process Data Cleanse Data Mining 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

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

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 Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

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. Find sources of relevant data. Choose data collection methods and tools.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 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. Another type of data storage — a data lake — tried to address these and other issues.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

MARCH 14, 2023

Batch jobs are often scheduled to load data into the warehouse, while real-time data processing can be achieved using solutions like Apache Kafka and Snowpipe by Snowflake to stream data directly into the cloud warehouse. But this distinction has been blurred with the era of cloud data warehouses.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Edureka

JUNE 1, 2023

No infrastructure to maintain and scale : The customers just need to store, process, and analyze big data. Advanced Analytical Tools: AWS has an ecosystem of analytical solutions that are specifically designed to manage the escalating volume of data and provide business intelligence.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

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

ProjectPro

JANUARY 31, 2022

This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse? A Data Warehouse is a central information repository that enables Data Analytics and Business Intelligence (BI) activities. Unlock the ProjectPro Learning Experience for FREE How Does Snowflake Store Data Internally?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Rockset

JULY 29, 2022

This fast, serverless, highly scalable, and cost-effective multi-cloud data warehouse has built-in machine learning, business intelligence, and geospatial analysis capabilities for querying massive amounts of structured and semi-structured data.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

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? Initially, DWs dealt with structured data presented in tabular forms.

Data Lake 64

Data Lake Data Warehouse ETL Tools Telecommunication 64

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard? Rise of the Data Lakehouse Data warehouse or data lake?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below. Here are some of the most common solutions that are involved in modern data pipelines and the role they play. Let the data drive the data pipeline architecture.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Knowledge Hut

DECEMBER 29, 2023

The highlight feature of this platform is its potential to integrate semi-structured and structured data without using any third-party tools. Apache Hive It is a Hadoop-based data management and storage tool that allows data analytics through an SQL-like framework.

Data Engineering 40

Data Engineering Data Engineer Engineering BI 40

AltexSoft

JULY 18, 2023

Spark SQL brings native support for SQL to Spark and streamlines the process of querying semistructured and structured data. Hadoop YARN : Often the preferred choice due to its scalability and seamless integration with Hadoop’s data storage systems, ideal for larger, distributed workloads.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. And most of this data has to be handled in real-time or near real-time.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 24, 2023

Furthermore, BigQuery supports machine learning and artificial intelligence, allowing users to use machine learning models to analyze their data. BigQuery Storage BigQuery leverages a columnar storage format to efficiently store and query large amounts of data. Q: Which two services does BigQuery provide?

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

AltexSoft

SEPTEMBER 10, 2021

So, why does anyone need to integrate data in the first place? Today, companies want their business decisions to be driven by data. But here’s the thing — information required for business intelligence (BI) and analytics processes often lives in a breadth of databases and applications. How data consolidation works.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 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. Definitely, not.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

AUGUST 18, 2021

Ace your next data analyst interview with these Tableau interview questions and answers that cover all the important topics and concepts in Tableau. Tableau is one of the most significant data visualization and business intelligence tools used by organizations across industries. What are the important topics in Tableau?

BI 40

BI Database-centric SQL Software Engineer 40

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. This noticeably saves time on copying and drastically reduces data storage costs.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

ProjectPro

JULY 27, 2021

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc. Structured data usually consists of only text.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40