Knowledge Hut

SEPTEMBER 26, 2023

The demand for knowledgeable data engineers that can plan, create, and maintain sophisticated data infrastructure is growing as the amount of data created by enterprises continues to increase dramatically. The success of our career as an Azure Data Engineer depends on our ability to master several different talents.

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Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. We were developing new skills, new ways of doing things, new tools, and — more often than not — turning our backs to traditional methods. We were data engineers! Data Engineering?

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Netflix Tech

JULY 15, 2021

Data Engineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Kevin, what drew you to data engineering?

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Knowledge Hut

NOVEMBER 17, 2023

The demand for data-related professions, including data engineering, has indeed been on the rise due to the increasing importance of data-driven decision-making in various industries. Becoming an Azure Data Engineer in this data-centric landscape is a promising career choice.

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ProjectPro

FEBRUARY 21, 2023

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? The answer is- by earning professional data engineering certifications! AWS or Azure?

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ProjectPro

FEBRUARY 8, 2023

Demand for ETL Developer Jobs With the increasing importance of ETL in data management for data-driven decision-making, the need for ETL developers is likely to grow in the coming years. Gartner lists ETL among the top 10 in-demand skills for big data professionals.

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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 66

ProjectPro

JANUARY 24, 2023

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. Learn more about Big Data Tools and Technologies with Innovative and Exciting Big Data Projects Examples.

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ProjectPro

JANUARY 24, 2023

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. You can leverage AWS Glue to discover, transform, and prepare your data for analytics. PREVIOUS NEXT <

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ProjectPro

FEBRUARY 16, 2023

ETL is a critical component of success for most data engineering teams, and with teams harnessing it with the power of AWS, the stakes are higher than ever. Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand.

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Data Engineering Weekly

DECEMBER 29, 2022

I even authored a special edition capturing metadata tool’s history at Data Engineering Weekly. Data Engineering Weekly Data Engineering Weekly #21: Metadata Edition Welcome to the 21st edition of the data engineering newsletter. The pre-modern(?) What does that mean?

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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

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Databand.ai

JUNE 14, 2023

The primary goal of data quality testing is to ensure that an organization’s datasets are accurate and reliable enough to support well-informed decisions. Data engineers often utilize specialized tools to perform comprehensive tests on their datasets while tracking metrics related to these aspects throughout the entire lifecycle.

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DareData

JULY 5, 2023

It provides a Python-native and highly flexible framework for building, scheduling, and monitoring data pipelines. Exploring these tools should give you a very cool overview of ETL tools being used in the market today. If you need help to understand how these tools work, feel free to drop us a message!

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ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline? What is a Big Data Pipeline?

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Knowledge Hut

MARCH 22, 2024

During the era of big data and real-time analytics, businesses face challenges, and the need for skilled MongoDB professionals has grown to an order of magnitude. Education & Skills Required Bachelor’s or Master’s degree in Computer Science, Data Science , or a related field. Good Hold on MongoDB and data modeling.

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ProjectPro

SEPTEMBER 26, 2021

The Big Data industry will be $77 billion worth by 2023. According to a survey, big data engineering job interviews increased by 40% in 2020 compared to only a 10% rise in Data science job interviews. Table of Contents Big Data Engineer - The Market Demand Who is a Big Data Engineer?

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Monte Carlo

DECEMBER 19, 2022

Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. According to Gartner , bad data costs organizations on average an estimated $12.9 Those are some big numbers. When it comes to data engineering, quality issues are a fact of life.

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Knowledge Hut

MARCH 26, 2024

Education & Skills Required: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Extensive experience in data architecture, database design, and data warehousing. Proficiency in database technologies such as SQL, NoSQL, and Big Data platforms.

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Edureka

FEBRUARY 9, 2023

The role of Azure Data Engineer is in high demand in the field of data management and analytics. As an Azure Data Engineer, you will be in charge of designing, building, deploying, and maintaining data-driven solutions that meet your organization’s business needs. Who is an Azure Data Engineer?

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AltexSoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

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Data Engineering Data Engineer Engineering Algorithm 98

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

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Knowledge Hut

NOVEMBER 19, 2023

One of the most important responsibilities for experts in big data is configuring the cloud to store data and provide high availability. As a result, data engineers working with big data today require a basic grasp of cloud computing platforms and tools. What Are Azure Data Engineer Tools?

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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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Towards Data Science

APRIL 6, 2023

And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them). So, join me on this post to develop a full data pipeline from scratch using some pieces from the AWS toolset.

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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? is a big advocate for Pig Latin. Their data engineers use Pig for data processing on their Hadoop clusters.

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Hadoop Unstructured Data Java SQL 52

Silectis

MARCH 9, 2021

The importance of data engineering is on the rise, with organizations increasingly investing in talent and infrastructure. I caught up with a few members of the team to take note of some of the data engineering trends we anticipate seeing more of this year and beyond. – Matt Boegner , Data Architect at Silectis 2.

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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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ProjectPro

JANUARY 27, 2023

Whether you are a data engineer, BI engineer, data analyst, or an ETL developer, understanding various ETL use cases and applications can help you make the most of your data by unleashing the power and capabilities of ETL in your organization. Well, it surely is!

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BI ETL Tools Retail Healthcare 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.

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Data Science Blog: Data Engineering

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

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ProjectPro

FEBRUARY 18, 2022

The big data analytics market is expected to grow at a CAGR of 13.2 This indicates that more businesses will adopt the tools and methodologies useful in big data analytics, including implementing the ETL pipeline. Table of Contents Why is ETL used in Data Science? percent, reaching USD 549.73

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AltexSoft

OCTOBER 8, 2021

But more often than not data is scattered across a myriad of disparate platforms, databases, and file systems. What’s more, that data comes in different forms and its volumes keep growing rapidly every day — hence the name of Big Data. Also, solutions provide automated data mapping. Data profiling and cleansing.

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AltexSoft

OCTOBER 21, 2022

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift.

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ProjectPro

NOVEMBER 30, 2021

If you are into Data Science or Big Data, you must be familiar with an ETL pipeline. This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. It is the most feasible option when the data size is huge.

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AltexSoft

AUGUST 4, 2021

For example, a company has a data mart containing all the financial data. The company may wish to model an OLAP cube to summarize this data by different dimensions: by time, by product, or by city, to name a few. Watch our video about data engineering to learn more about how data gets from sources to BI tools.

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Data Lake Data Warehouse ETL Tools Telecommunication 64

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. This transformation could involve cleaning, aggregating, or summarizing the data.

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