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.

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Data Engineering Data Engineer Engineering Banking 52

Data Engineering Podcast

FEBRUARY 9, 2020

Summary Designing the structure for your data warehouse is a complex and challenging process. As businesses deal with a growing number of sources and types of information that they need to integrate, they need a data modeling strategy that provides them with flexibility and speed.

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Data Lake Data Warehouse Hadoop NoSQL 100

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 Query Google’s cloud data warehouse.

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Data Engineering Data Engineer Engineering Hadoop 40

Edureka

FEBRUARY 7, 2023

If you want to become a data engineer, you should prepare for the interview process. To help you get a head start on your preparation, I’ve compiled a list of the Top 30+ Azure Data Engineer Interview Questions. When it comes to professionals, data engineers are the most in-demand in the IT industry.

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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?

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Data Engineering Data Engineer Engineering Healthcare 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.

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Data Science BI Business Intelligence Data Mining 97

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

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Raw Data Data Warehouse Data Cleanse Data Integration 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.

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Data Collection Machine Learning Unstructured Data Non-relational Database 52

Rockset

JANUARY 5, 2022

You may think that engineering teams would favor building on SQL databases as much as possible, given their rich heritage. Yet, when I talk to CTOs and VPs of engineering, I continually hear three myths about how SQL databases can’t possibly support real-time analytics well. of developers. Newer SQL databases are built differently.

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Database SQL NoSQL Raw Data 52

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

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Unstructured Data NoSQL Hadoop Data Lake 59

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. Data storage and processing.

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Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

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Bytes Google Cloud Data Warehouse Datasets 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.

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Data Engineering Data Engineer Engineering Scala 52

AltexSoft

NOVEMBER 10, 2021

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. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

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Architecture Data Lake Data Warehouse Metadata 52

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.

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Data Science Data Mining Deep Learning Programming Language 98

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.

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IT Data Warehouse Data Governance Data Lake 59

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 can be used to create data lakes, populate data warehouses or for specific use cases like offloading analytics and text search. Documents in MongoDB can also have complex structures.

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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.

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Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

SEPTEMBER 9, 2021

Spark SQL, for instance, enables structured data processing with SQL. Explore SQL Database Projects to Add them to Your Data Engineer Resume. The datasets are usually present in Hadoop Distributed File Systems and other databases integrated with the platform. Similarly, GraphX is a valuable tool for processing graphs.

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Hadoop Big Data Tools Java SQL 52

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

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Architecture Data Lake Medical Datasets 52

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

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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.

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Hadoop Big Data Google Cloud NoSQL 59

Rockset

OCTOBER 20, 2021

Until now, the majority of the world’s data transformations have been performed on top of data warehouses, query engines, and other databases which are optimized for storing lots of data and querying them for analytics occasionally. For instance, let’s say you have streaming data coming in from Kafka or Kinesis.

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SQL PostgreSQL MongoDB NoSQL 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.

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Big Data Hadoop AWS Relational Database 52

AltexSoft

JULY 27, 2021

At the same time, the continuous growth of available data has led to information overload — when there are too many choices, complicating decision-making. Recommender systems , also known as recommender engines, are information filtering systems that provide individual recommendations in real-time. implicit data or behavior data (e.g.,

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Machine Learning Systems Algorithm Deep Learning 76

ProjectPro

NOVEMBER 15, 2021

It serves as a distributed processing engine for both categories of data streams: unbounded and bounded. With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. It offers a fault-tolerant storage engine that prioritizes data security.

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Big Data Project Metadata Programming Language 52

ProjectPro

APRIL 27, 2016

In the data preparation phase (popularly known as the ETL phase) where raw data is cleaned to produce datasets that users can consume for analysis –Apache Pig along with a workflow system like Oozie can be a good choice because users of data preparation phase are usually researchers, data specialists or engineers.

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ProjectPro

OCTOBER 15, 2014

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. Their data engineers use Pig for data processing on their Hadoop clusters. d) Hive allows its users to embed customized mappers and reducers.

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Hadoop Unstructured Data Java SQL 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. HBase is a NoSQL database. Hcatalog can be used to share data structures with external systems.

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Hadoop Metadata SQL Database 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.

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Hadoop Architecture IT Java 52

ProjectPro

NOVEMBER 14, 2015

From Data Engineering Fundamentals to full hands-on example projects , check out data engineering projects by ProjectPro 2. Data Integration Businesses seldom start big. Streaming analytics is a real-time analysis of data streams that must (almost instantaneously) report abnormalities and trigger suitable actions.

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Hadoop Project Big Data Healthcare 52

Netflix Tech

DECEMBER 14, 2018

data access semantics that guarantee repeatable data read behavior for client applications. System Requirements Support for Structured Data The growth of NoSQL databases has broadly been accompanied with the trend of data “schemalessness” (e.g., a powerful and feature rich indexing and searching engine.

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Media Database Metadata Data Schemas 94

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.

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