Knowledge Hut

DECEMBER 26, 2023

According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 10 9 gigabytes) globally by the year 2025. Thus, almost every organization has access to large volumes of rich data and needs “experts” who can generate insights from this rich data.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

ProjectPro

SEPTEMBER 26, 2014

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Big data, multi-structured data, and advanced analytics.

Business Intelligence 40

Business Intelligence Hadoop BI Relational Database 40

ProjectPro

JANUARY 27, 2023

All this is possible now, thanks to the versatile data science applications in the finance industry. Join us as we highlight some of the most popular data science applications in finance and exciting project ideas for financial data scientists to help them stay ahead of the curve.

Finance 52

Finance Data Science Unstructured Data Algorithm 52

Knowledge Hut

JUNE 20, 2023

The generalist position would suit a data scientist looking for a transition into a data engineer. Pipeline-Centric Engineer: These data engineers prefer to serve in distributed systems and more challenging projects of data science with a midsize data analytics team.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

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? This statistic probably comes as no surprise.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 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

Knowledge Hut

JANUARY 19, 2024

With businesses relying heavily on data, the demand for skilled data scientists has skyrocketed. In data science, we use various tools, processes, and algorithms to extract insights from structured and unstructured data. Enhances skills in managing and processing big data efficiently.

Data Science 52

Data Science Machine Learning Datasets Algorithm 52

Knowledge Hut

JULY 4, 2023

Data Architect Data architects design and construct data management and storage systems blueprints. While this job does not directly involve extracting insights from data, you must be familiar with the analysis process. It is a must to build appropriate data structures.

Data Analytics 52

Data Analytics Data Architect Transportation Data Science 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. ETL is the acronym for Extract, Transform, and Load.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

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.

Hadoop 52

Hadoop Unstructured Data ETL Tools ETL System 52

ProjectPro

JULY 22, 2016

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream data –Data Lakes fulfil a critical need.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

JUNE 9, 2021

Let's now look at the two data science job roles- Data Engineer and Data Scientist. A data scientist creates programming code and combines it with statistical knowledge to develop insights into business data. Data mining and data management skills are essential for a data engineer.nd

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Data Engineering Podcast

JUNE 26, 2022

In this episode Isaac Brodsky explains how the Unfolded platform is architected, their experience joining the team at Foursquare, and how you can start using it for analyzing your spatial data today. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Datasets 130

Datasets Unstructured Data Metadata MongoDB 130

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 Unstructured Data 40

Data Engineering Podcast

NOVEMBER 27, 2022

This has led to inefficiencies in how data is stored, accessed, and shared across process and system boundaries. The Arrow project is designed to eliminate wasted effort in translating between languages, and Voltron Data was created to help grow and support its technology and community.

Data Process 100

Data Process Process Metadata Business Intelligence 100

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

Knowledge Hut

DECEMBER 22, 2023

Statistics are used by data scientists to collect, assess, analyze, and derive conclusions from data, as well as to apply quantifiable mathematical models to relevant variables. Microsoft Excel An effective Excel spreadsheet will arrange unstructured data into a legible format, making it simpler to glean insights that can be used.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

AltexSoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Edureka

FEBRUARY 9, 2023

Data Governance and Security: Azure Data Engineers should have a strong understanding of data governance and security principles, as well as experience with Azure Security Center and Azure Active Directory. Develop JSON scripts for deploying pipelines in Azure Data Factory (ADF) that process data using SQL activities.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 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. What does a Data Processing Analysts do ?

Data Process 52

Data Process Process Data Cleanse Data Mining 52

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. Big Data technologies are now being used in multiple industries and business sectors.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 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. Business Intelligence - ETL is a key component of BI systems for extracting and preparing data for analytics.

BI 52

BI ETL Tools Retail Healthcare 52

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. Skills acquired : Core data concepts. Data storage options.

Database 52

Database Certification MongoDB MySQL 52

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 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

ProjectPro

JUNE 24, 2021

Table of Contents Skills Required for Data Analytics Jobs Why Should Students Work on Big Data Analytics Projects ? 10+ Real-Time Azure Project Ideas for Beginners to Practice Access Job Recommendation System Project with Source Code Why Should Students Work on Big Data Analytics Projects ?

Data Analytics 40

Data Analytics Project Insurance Hadoop 40

ProjectPro

AUGUST 26, 2015

Here’s a look at important milestones, tracking the evolutionary progress on how data has been collected, stored, managed and analysed- 1926 – Nikola Tesla predicted that humans will be able to access and analyse huge amounts of data in the future by using a pocket friendly device. 1937 - Franklin D. Truskowski.

Big Data 40

Big Data Unstructured Data Hadoop NoSQL 40

AltexSoft

OCTOBER 3, 2022

This post aims to dive deeper into the topic and give a complete picture of what knowledge graphs are, their components and concepts, and how businesses can use them for better decision-making and growth. So, if you want to apply this technology in your projects but have no idea where to start, this post is a great place.

Relational Database 52

Relational Database Banking Media Computer Science 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. Scalability.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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. contributors.

Engineering 52

Engineering NoSQL Programming Language Java 52

AltexSoft

MAY 14, 2021

What is Big Data analytics? Big Data analytics is the process of finding patterns, trends, and relationships in massive datasets that can’t be discovered with traditional data management techniques and tools. The best way to understand the idea behind Big Data analytics is to put it against regular data analytics.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

NOVEMBER 30, 2021

ETL (Extract, Transform, and Load) Pipeline involves data extraction from multiple sources like transaction databases, APIs, or other business systems, transforming it, and loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

Process 52

Process Data Pipeline Data Warehouse AWS 52

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 AWS Relational Database 52

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

DECEMBER 26, 2023

Big Data is an immense amount of data that is constantly growing exponentially. Due to its vastness and complexity, no traditional data management system can adequately store or process this data. The New York Stock Exchange, which generates one terabyte of new trade data each day, is a classic example of big 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

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