Gerhard  Brink

Gerhard Brink

1621509600

Data Engineers: Myths vs. Realities

From self-driven cars to automatic tagging in images, data science has come a long way. Data scientists and analysts have become an integral part of any organisation because of the value they add. But, in all honesty, a data scientist is only as good as the data they work with. Most of the organisations today have their data stored in a variety of formats and across numerous platforms. Here comes a need for data engineers!

Data engineers are people who make this data workable for the data scientists and analysts. Data engineers are responsible for building pipelines that transform the heaps of data into a format that is usable for data scientists. They mostly work behind the scenes and hence are devoid of all the glamors of a data scientist/analyst – but mind you, they’re equally (if not more) essential to the functioning of any organisation.

If data scientists are race car drivers, data engineers are race car builders. The former gets the excitement of speeding along a track and thrill of winning in front of an applauding crowd. The latter, on the other hand, gets the joy of tuning engines and creating a powerful, robust machine. A race car builder makes the job of the driver a lot easier (or tougher, depending on the quality of the builder).

#data science #big data #data #data analytics #myth busters

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Data Engineers: Myths vs. Realities
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1621413060

Top 5 Exciting Data Engineering Projects & Ideas For Beginners [2021]

Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it.

You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience.

Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

That’s because you’ll need to complete the projects correctly. Here are the most important ones:

  • Python and its use in big data
  • Extract Transform Load (ETL) solutions
  • Hadoop and related big data technologies
  • Concept of data pipelines
  • Apache Airflow

#big data #big data projects #data engineer #data engineer project #data engineering projects #data projects

 iOS App Dev

iOS App Dev

1624072920

10 Must-have Skills for Data Engineering Jobs

Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.

Here, we list some of the important skills that one should possess to build a successful career in big data.

1. Database Tools
2. Data Transformation Tools
3. Data Ingestion Tools
4. Data Mining Tools

#big data #latest news #data engineering jobs #skills for data engineering jobs #10 must-have skills for data engineering jobs #data engineering

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Gerhard  Brink

Gerhard Brink

1621509600

Data Engineers: Myths vs. Realities

From self-driven cars to automatic tagging in images, data science has come a long way. Data scientists and analysts have become an integral part of any organisation because of the value they add. But, in all honesty, a data scientist is only as good as the data they work with. Most of the organisations today have their data stored in a variety of formats and across numerous platforms. Here comes a need for data engineers!

Data engineers are people who make this data workable for the data scientists and analysts. Data engineers are responsible for building pipelines that transform the heaps of data into a format that is usable for data scientists. They mostly work behind the scenes and hence are devoid of all the glamors of a data scientist/analyst – but mind you, they’re equally (if not more) essential to the functioning of any organisation.

If data scientists are race car drivers, data engineers are race car builders. The former gets the excitement of speeding along a track and thrill of winning in front of an applauding crowd. The latter, on the other hand, gets the joy of tuning engines and creating a powerful, robust machine. A race car builder makes the job of the driver a lot easier (or tougher, depending on the quality of the builder).

#data science #big data #data #data analytics #myth busters