Knowledge Hut

MARCH 13, 2024

Wondering what is a big data engineer? As the name suggests, Big Data is associated with ‘big’ data, which hints at something big in the context of data. Big data forms one of the pillars of data science. Big data has been a hot topic in the IT sector for quite a long time.

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Knowledge Hut

MARCH 13, 2024

Wondering what is a big data engineer? As the name suggests, Big Data is associated with ‘big’ data, which hints at something big in the context of data. Big data forms one of the pillars of data science. Big data has been a hot topic in the IT sector for quite a long time.

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Big Data Data Engineering Data Engineer Engineering 52

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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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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Meltano

OCTOBER 5, 2022

Interested in becoming a data engineer? The need for data experts in the U.S. job market is expected to grow by 22% in this decade, and according to LinkedIn’s 2020 report , a data engineer is listed as the 8th fastest growing job today. But what is data engineering exactly and what does a data engineer do?

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Knowledge Hut

OCTOBER 3, 2023

Within the Microsoft Azure ecosystem, the role of an Azure data engineer stands out as one of the most sought-after positions. What Does an Azure Data Engineer Do? Azure Data engineers collaborate with Azure AI services built on top of Azure Cognitive Services APIs to offer end customers a variety of pre-built models.

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Data Engineering Data Engineer Engineering Cloud Computing 52

Knowledge Hut

NOVEMBER 28, 2023

The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Data Engineer certification will aid in scaling up you knowledge and learning of data engineering.

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Data Engineering Data Engineer Engineering Generalist 52

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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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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Data Engineering Data Engineer Engineering Data Mining 52

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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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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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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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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Ascend.io

DECEMBER 19, 2023

We’ll talk about when and why ETL becomes essential in your Snowflake journey and walk you through the process of choosing the right ETL tool. Our focus is to make your decision-making process smoother, helping you understand how to best integrate ETL into your data strategy. But first, a disclaimer.

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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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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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Silectis

DECEMBER 15, 2020

If you’ve been following along with Silectis over the past couple of years, you are familiar with our data engineering platform, Magpie. You’re aware of the many outcomes it puts at the fingertips of data engineers, and teams of data practitioners more largely. If you’re new around here, not to worry.

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Data Engineering Data Engineer Engineering ETL Tools 52

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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Rockset

FEBRUARY 17, 2022

To solve this last mile problem and ensure your data models actually get used by business team members, you need to sync data directly to the tools your business team members use day-to-day, from CRMs like Salesforce to ad networks, email tools and more. As data engineers, how we engineer said data is important.

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DataKitchen

FEBRUARY 21, 2023

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Data team morale is consistent with DataKitchen’s own research. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.

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ProjectPro

FEBRUARY 8, 2023

Data Integration and Transformation, A good understanding of various data integration and transformation techniques, like normalization, data cleansing, data validation, and data mapping, is necessary to become an ETL developer. Extract, transform, and load data into a target system.

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ProjectPro

SEPTEMBER 26, 2021

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? Most of these are performed by Data Engineers.

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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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Monte Carlo

DECEMBER 4, 2023

At the heart of data engineering lies the ETL process—a necessary, if sometimes tedious, set of operations to move data across pipelines for production. So, don’t forget to review your ChatGPT outputs before leverage scripts or pushing any SQL code to production.

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Ascend.io

MAY 31, 2023

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Data Ingestion Data ingestion is the first step of both ETL and data pipelines. The data sources themselves are not built to perform analytics.

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Ascend.io

DECEMBER 21, 2022

After, they leverage the power of the cloud warehouse to perform deep analysis, build predictive models, and feed BI tools and dashboards. However, data warehouses are only accessible to technical users who know how to write SQL. As a result, you have to use reverse ELT, which is essentially writing reverse SQL.

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Ascend.io

AUGUST 31, 2023

Performance: Because the data is transformed and normalized before it is loaded , data warehouse engines can leverage the predefined schema structure to tune the use of compute resources with sophisticated indexing functions, and quickly respond to complex analytical queries from business analysts and reports.

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Cloudera

JUNE 26, 2023

Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. Take precaution using CDSW as an all-purpose workflow management and scheduling tool. So which open source pipeline tool is better, NiFi or Airflow?

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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 Podcast

JANUARY 15, 2022

In this episode Brian McMillan shares his work on the book "Building Data Products" and how he is working to educate business users and data professionals about the combination of technical, economical, and business considerations that need to be blended for these projects to succeed.

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Data Science Blog: Data Engineering

SEPTEMBER 19, 2023

So why using IaC for Cloud Data Infrastructures? For Data Warehouse Systems that often require powerful (and expensive) computing resources, this level of control can translate into significant cost savings. The following Terraform script will create an Azure Resource Group, a SQL Server, and a SQL Database.

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Monte Carlo

JANUARY 25, 2023

Freshness checks validate the quality of data within a table by monitoring how frequently that data is updated against predefined latency rules, such as when you expect an ingestion job to load on any given day. This test can be challenging if the data is coming from multiple sources or if the data is updated frequently.

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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. Both AWS Glue and Azure Data Factory can import SSIS packages. PREVIOUS NEXT <

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Monte Carlo

NOVEMBER 20, 2023

Far from a mere “copy-paste” job, data migrations can be deceptively tricky and one of the most challenging aspects of a data engineer’s job. But as businesses pivot and technologies advance, data migrations are—regrettably—unavoidable. Flyway : Manages and tracks database schema changes, ensuring smooth migrations.

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Knowledge Hut

MAY 2, 2024

As per Apache, “ Apache Spark is a unified analytics engine for large-scale data processing ” Spark is a cluster computing framework, somewhat similar to MapReduce but has a lot more capabilities, features, speed and provides APIs for developers in many languages like Scala, Python, Java and R.

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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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Monte Carlo

SEPTEMBER 8, 2021

It’s a new approach to making data actionable and solving the “last mile” problem in analytics by empowering business teams to access—and act on—transformed data directly in the SaaS tools they already use every day. This frees up data engineers from one-off tasks while enabling business teams to self-serve their data.

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