ProjectPro

JANUARY 31, 2023

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. Get ready to expand your knowledge and take your big data career to the next level! Everything is about data these days.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

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Big Data Data Analytics IT NoSQL 78

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Which Big Data tasks does Spark solve most effectively? How does it work?

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

AltexSoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. 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.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

MAY 23, 2024

It uses batch processing to handle this flow of enormous data streams (that are unbounded - i.e., they do not have a fixed start and endpoint) as well as stored datasets (that are bounded). Python: Python is, by far, the most widely used data science programming language. Big Data Tools 23.

Data Science 52

Data Science MongoDB Programming Language Unstructured Data 52

Knowledge Hut

DECEMBER 26, 2023

They are also accountable for communicating data trends. Let us now look at the three major roles of data engineers. Generalists They are typically responsible for every step of the data processing, starting from managing and making analysis and are usually part of small data-focused teams or small companies.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Knowledge Hut

APRIL 26, 2024

In the age of big data processing, how to store these terabytes of data surfed over the internet was the key concern of companies until 2010. Now that the issue of storage of big data has been solved successfully by Hadoop and various other frameworks, the concern has shifted to processing these data.

Data Science 52

Data Science Machine Learning Hadoop Programming Language 52

Knowledge Hut

DECEMBER 26, 2023

They identify business problems and opportunities to enhance the practices, processes, and systems within an organization. Using Big Data, they provide technical solutions and insights that can help achieve business goals. They are also responsible for improving the performance of data pipelines.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

Knowledge Hut

NOVEMBER 28, 2023

Data Engineers are professionals who bridge the gap between the working capacity of software engineering and programming. They are people equipped with advanced analytical skills, robust programming skills, statistical knowledge, and a clear understanding of big data technologies. Technical Data Engineer Skills 1.Python

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Edureka

JANUARY 18, 2024

This guide provides a comprehensive understanding of the essential skills and knowledge required to become a successful data scientist, covering data manipulation, programming, mathematics, big data, deep learning, and machine learning technologies. Table of Contents Introduction to Data Science What is Data Science?

Data Science 40

Data Science Deep Learning NoSQL Machine Learning 40

Knowledge Hut

DECEMBER 29, 2023

Data scientists may improve their knowledge and response to crucial business demands by opting to specialize in a subfield of their subject. It's possible they'll zero down on a certain data kind, like Big Data, or a computer language. One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Knowledge Hut

JULY 24, 2023

The future of SQL (Structured Query Language) is a scalding subject among professionals in the data-driven world. As data generation continues to skyrocket, the demand for real-time decision-making, data processing, and analysis increases. However, SQL is more comprehensive than these traditional uses.

Database 52

Database SQL Process NoSQL 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

SEPTEMBER 9, 2021

Apache Hive and Apache Spark are the two popular Big Data tools available for complex data processing. To effectively utilize the Big Data tools, it is essential to understand the features and capabilities of the tools. Spark SQL, for instance, enables structured data processing with SQL.

Hadoop 52

Hadoop Big Data Tools Java SQL 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

ProjectPro

MARCH 16, 2015

There is a lot of buzz around big data making the world a better place and the best example to understand this is analysing the uses of big data in healthcare industry. Here begins the journey through big data in healthcare highlighting the prominently used applications of big data in healthcare industry.

Healthcare 40

Healthcare Big Data Project Hospitality 40

ProjectPro

JANUARY 24, 2023

Additionally, columnar storage allows BigQuery to compress data more effectively, which helps to reduce storage costs. BigQuery enables users to store data in tables, allowing them to quickly and easily access their data. It supports structured and unstructured data, allowing users to work with various formats.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

Knowledge Hut

MARCH 20, 2024

Builds and manages data processing, storage, and management systems. They are responsible for establishing and managing data pipelines that make it easier to gather, process, and store large volumes of structured and unstructured data. Authorization and user authentication across servers and systems.

Software Engineer 52

Software Engineer Software Engineering Engineering Java 52

ProjectPro

JUNE 2, 2015

2015 will be the year that many big data companies will take their big data analytics to the next level by turning big data into actionable insights. This will supercharge the marketing tactics of the business and make data precious than ever. May 6, UK IT News V3.co.uk

Big Data 40

Big Data Hadoop Banking Retail 40

Knowledge Hut

NOVEMBER 7, 2023

The applications of cloud computing in businesses of all sizes, types, and industries for a wide range of applications, including data backup, email, disaster recovery, virtual desktops big data analytics, software development and testing, and customer-facing web apps.

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services Entertainment 98

Knowledge Hut

JUNE 16, 2023

Data preparation: Because of flaws, redundancy, missing numbers, and other issues, data gathered from numerous sources is always in a raw format. After the data has been extracted, data analysts must transform the unstructured data into structured data by fixing data errors, removing unnecessary data, and identifying potential data.

Programming Language 52

Programming Language Cloud Computing Data Preparation Data Science 52

Rockset

NOVEMBER 1, 2018

Over a decade after the inception of the Hadoop project, the amount of unstructured data available to modern applications continues to increase. This longevity is a testament to the community of analysts and data practitioners who are familiar with SQL as well as the mature ecosystem of tools around the language.

Raw Data 40

Raw Data SQL Unstructured Data NoSQL 40

ProjectPro

JANUARY 19, 2022

Read this blog till the end to learn more about the roles and responsibilities, necessary skillsets, average salaries, and various important certifications that will help you build a successful career as an Azure Data Engineer. The big data industry is flourishing, particularly in light of the pandemic's rapid digitalization.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

AltexSoft

NOVEMBER 10, 2021

Key data warehouse limitations: Inefficiency and high costs of traditional data warehouses in terms of continuously growing data volumes. Inability to handle unstructured data such as audio, video, text documents, and social media posts. websites, etc. websites, etc. Storage layer.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

DECEMBER 23, 2022

ELT makes it easier to manage and access all this information by allowing both raw and cleaned data to be loaded and stored for further analysis. With the ETL shift from a traditional on-premise variant to a cloud solution, you can also use it to work with different data sources and move a lot of data.

Process 52

Process Building Raw Data Data Lake 52

AltexSoft

SEPTEMBER 21, 2023

In this edition of “The Good and The Bad” series, we’ll dig deep into Elasticsearch — breaking down its functionalities, advantages, and limitations to help you decide if it’s the right tool for your data-driven aspirations. As a result, Elasticsearch is exceptionally efficient in managing structured and unstructured data.

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

NOVEMBER 5, 2014

MongoDB NoSQL database is used in the big data stack for storing and retrieving one item at a time from large datasets whereas Hadoop is used for processing these large data sets. For organizations to keep the load off MongoDB in the production database, data processing is offloaded to Apache Hadoop.

MongoDB 40

MongoDB Hadoop NoSQL Big Data 40

ProjectPro

JULY 27, 2021

In this blog, we have collated the frequently asked data engineer interview questions based on tools and technologies that are highly useful for a data engineer in the Big Data industry. that leverage big data analytics and tools. Preparing for data engineer interviews makes even the bravest of us anxious.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

APRIL 1, 2016

Syncsort has delivered this because some of the companies in industries like financial services, banking, and insurance needed to maintain their mainframe data in native format. Source: [link] ) Cloudera has been named a visionary in the Big Data Space in the 2016 Gartner Magic Quadrant. March 7, 2016. March 11, 2016.

Hadoop 52

Hadoop BI Big Data Big Data Tools 52

Databand.ai

DECEMBER 13, 2022

Follow Charles on LinkedIn 3) Deepak Goyal Azure Instructor at Microsoft Deepak is a certified big data and Azure Cloud Solution Architect with more than 13 years of experience in the IT industry. On LinkedIn, he focuses largely on Spark, Hadoop, big data, big data engineering, and data engineering.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Edureka

FEBRUARY 7, 2023

Azure Synapse Interview Questions – Analytics The interview questions and responses for azure data engineers for synapse analytics and stream analytics are covered in this section. 6) Which Azure service would you use to build a data warehouse? 14) What are Azure Databricks, and how are they unique from standard data bricks?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Storage Layer: This is a centralized repository where all the data loaded into the data lake is stored.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. Scalability 4.Link

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

OCTOBER 15, 2014

Table of contents Hive vs Pig What is Big Data and Hadoop? Not only this, few of the people are as well of the thought that Big Data and Hadoop are one and the same. What is Big Data and Hadoop? Difference between pig and hive is Pig needs some mental adjustment for SQL users to learn.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

ProjectPro

JUNE 4, 2015

The basic principle of working behind Apache Hadoop is to break up unstructured data and distribute it into many parts for concurrent data analysis. Big data applications using Apache Hadoop continue to run even if any of the individual cluster or server fails owing to the robust and stable nature of Hadoop.

Hadoop 52

Hadoop Architecture IT Java 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52