Knowledge Hut

MAY 4, 2023

Data engineering is a prominent yet emerging technological field that is currently enjoying high demand. Salaries for data engineers vary across the globe, depending on various factors such as location, experience, skills and Data Engineer training and certifications taken by the professionals.

Data Engineering 52

Data Engineering Data Engineer Engineering Banking 52

Knowledge Hut

DECEMBER 26, 2023

Data Science is a combination of several disciplines including Mathematics and Statistics, Data Analysis, Machine Learning, and Computer Science. Data Science is a huge umbrella with a plethora of roles available in the field such as a Data Scientist, Data Engineer, BI Developer, Data and Analytics Manager, etc.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

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. Data Engineering Data engineering is a process by which data engineers make data useful.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

FEBRUARY 16, 2023

Demand for data engineers is at a peak today globally due to the massive amount of data that companies accumulate and work with this data to draw actionable insights and make better business decisions. To do that, the generated data must be interpreted to be understood by the end user. What Does a Data Engineer Do?

Data Engineering 52

Data Engineering Data Engineer Engineering Healthcare 52

ProjectPro

JANUARY 19, 2022

Planning to land a successful job as an Azure Data Engineer? 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.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

AltexSoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Data engineering explained in 14 minutes.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

JULY 27, 2021

This blog is your one-stop solution for the top 100+ Data Engineer Interview Questions and Answers. 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.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

DECEMBER 29, 2023

There are many data science fields in which experts may contribute to the success of a business, and you can hone the abilities you need by specializing in data science subfields. Data Engineering and Warehousing The data is the lifeblood of every successful Data Science endeavor.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. 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. NoSQL databases.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Rockset

JULY 28, 2022

MongoDB has grown from a basic JSON key-value store to one of the most popular NoSQL database solutions in use today. This means analysts who are used to using SQL will have a steep learning curve for this new language. Documents in MongoDB can also have complex structures.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

ProjectPro

JANUARY 24, 2023

With BigQuery, users can process and analyze petabytes of data in seconds and get insights from their data quickly and easily. Moreover, BigQuery offers a variety of features to help users quickly analyze and visualize their data. It provides powerful query capabilities for running SQL queries to access and analyze data.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

AltexSoft

JUNE 7, 2021

MapReduce performs batch processing only and doesn’t fit time-sensitive data or real-time analytics jobs. Data engineers who previously worked only with relational database management systems and SQL queries need training to take advantage of Hadoop. Data storage options. Data access options.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

AltexSoft

JULY 29, 2022

What happens, when a data scientist, BI developer , or data engineer feeds a huge file to Hadoop? Under the hood, the framework divides a chunk of Big Data into smaller, digestible parts and allocates them across multiple commodity machines to be processed in parallel. How data engineering works under the hood.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Rockset

DECEMBER 31, 2022

Storage of inconsistent schema items If your data objects are required to be stored in inconsistent schemas, DynamoDB can manage that. This is not possible in the case of DynamoDB since it’s a non-relational database that works better with NoSQL formatted data tables.

Non-relational Database 52

Non-relational Database Healthcare Amazon Web Services NoSQL 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore).

Big Data 78

Big Data Data Management Management Metadata 78

ProjectPro

SEPTEMBER 9, 2021

Spark SQL, for instance, enables structured data processing with SQL. Highly flexible and scalable Real-time stream processing Spark Stream – Extension of Spark enables live-stream from massive data volumes from different web sources. Hive uses HQL, while Spark uses SQL as the language for querying the data.

Hadoop 52

Hadoop Big Data Tools Java SQL 52

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Ascend.io

NOVEMBER 21, 2023

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

AltexSoft

NOVEMBER 10, 2021

So, unlike data warehouses, the lakehouse system can store and process lots of varied data at a lower cost, and unlike data lakes, that data can be managed and optimized for SQL performance. Data warehouse vs data lake vs data lakehouse: What’s the difference. Data lake architecture example.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 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. Data is generally not loaded into a data warehouse unless a use case has been defined for the data.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Rockset

SEPTEMBER 3, 2020

PostgreSQL has been gaining a lot of traction recently because of its ability to provide both RDBMS-like and NoSQL-like features which enable data to be stored in traditional rows and columns while also providing the option to store complete JSON objects. To begin with, you can leverage the power of the SQL query language.

PostgreSQL 40

PostgreSQL MongoDB SQL Database 40

AltexSoft

JULY 19, 2022

So, to avoid any confusion, please be aware that data mesh is NOT. a data fabric, which is a single environment consisting of a unified architecture, and services or technologies running on that architecture. So, to avoid any confusion, please be aware that data mesh is NOT. And it’s their job to guarantee data quality.

Architecture 52

Architecture Data Lake Medical Datasets 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

OCTOBER 15, 2014

Makes use of exact variation of dedicated SQL DDL language by defining tables beforehand. Pig is SQL like but varies to a great extent. Directly leverages SQL and is easy to learn for database experts. Hive Query language (HiveQL) suits the specific demands of analytics meanwhile PIG supports huge data operation.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

AltexSoft

OCTOBER 8, 2021

It’s well-suited for everyone from data engineers to business users with little or no tech expertise. Data profiling and cleansing. Xplenty allows organizations that don’t have a data engineering team to perform data profiling and cleansing procedures automatically. They include NoSQL databases (e.g.,

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Rockset

SEPTEMBER 2, 2022

Prabhu has also worked in search and data engineering roles at Twitter, Google, and Microsoft. My co-founder at Rockset and CEO Venkat Venkataramani hosted a panel of data engineering experts who tackled the topic of SQL versus NoSQL databases in the modern data stack.

Systems 52

Systems Building Machine Learning NoSQL 52

Knowledge Hut

DECEMBER 26, 2023

However, there has been a lack of Big Data specialists in the IT industry, so if you want to become a Big Data Engineer , now is the time to acquire the required abilities. The abilities you must develop are as follows: coding abilities (Python, R, SQL, Scala, etc.)

Banking 52

Banking Insurance Finance Cloud Computing 52

ProjectPro

APRIL 26, 2016

Pig vs Hive Criteria Pig Hive Type of Data Apache Pig is usually used for semi structured data. Used for Structured Data Schema Schema is optional. Language It is a procedural data flow language. Follows SQL Dialect and is a declarative language. Hive allows execution of most SQL queries.

Hadoop 40

Hadoop Metadata SQL Database 40

ProjectPro

NOVEMBER 14, 2015

Hadoop ecosystem has a very desirable ability to blend with popular programming and scripting platforms such as SQL, Java , Python, and the like which makes migration projects easier to execute. From Data Engineering Fundamentals to full hands-on example projects , check out data engineering projects by ProjectPro 2.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

APRIL 27, 2016

Hadoop developers had to spend hours and days together to write MapReduce programs for basic analysis tasks, as Hadoop lacked SQL like expressibility power. Users of Apache Hive are usually decision makers, analysts or engineers using the data for their systems. Presto runs both in-memory and on disk.

Hadoop 40

Hadoop Big Data Technology Big Data Tools 40

ProjectPro

JUNE 4, 2015

Learn Hadoop to become a Microsoft Certified Big Data Engineer. With big data being used extensively to leverage analytics for gaining meaningful insights, Apache Hadoop is the solution for processing big data. It can also be used for exporting data from Hadoop o other external structured data stores.

Hadoop 52

Hadoop Architecture IT Java 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40