Big Data Project Guidelines

Cloud-based and big data-based projects require a lot of guidelines to keep all factors in check and accounted for — review the most important ones here.

The aim of the following article is to share with you some of the most relevant guidelines in cloud-based big data-based projects that I’ve done in my recent mission. The following list is not an exhaustive one and may be completed according to each organization/project specifications.

  • Guidelines for Cloud-Based and Data-Based Projects
    • Data Storage
    • Data Processing
    • Data Locality
    • Various

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Big Data Project Guidelines
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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

Ian  Robinson

Ian Robinson

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Managing the Big Data Project – Lifecycle, Approach, Team Composition, Pitfalls

Managing the Big Data project

Everything ‘expands’ in a Big Data project. There are many more decision points, even before you draw the first entity in your ERD design tool. A typical IT lifecycle, of any type, consists of

Analysis (“You start coding and I’ll go upstairs and see what the business wants”);
Design (“Is this a pattern that has previously been designed?”)
Coding (“Is this a pattern that has previously been coded?”)
Testing (“Have we used tests like these, before?”)
Implementation
Feedback (all positive, right?)
All of the above happens in Agile development as well as in Waterfall development, but for this discussion, I’ll ignore the details of Agile work sessions.
Let’s dive in the detailed lifecycle about Big Data projects

#big data #big data & technology #big data analysis #big data project #software testing #managing the big data project

Gerhard  Brink

Gerhard Brink

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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

Big Data Consulting Services | Big Data Development Experts USA

Big Data Consulting Services

Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.

Want to develop a Big Data Processing Application?

WebClues Infotech with its years of experience and serving 350+ clients since our inception is the agency to trust for the Big Data Processing Application development services. With a team that is skilled in the latest technologies, there can be no one better for fulfilling your development requirements.

Want to know more about our Big Data Processing App development services?

Visit: https://www.webcluesinfotech.com/big-data-solutions/

Share your requirements https://www.webcluesinfotech.com/contact-us/

View Portfolio https://www.webcluesinfotech.com/portfolio/

#big data consulting services #big data development experts usa #big data analytics services #big data services #best big data analytics solution provider #big data services and consulting

Silly mistakes that can cost ‘Big’ in Big Data Analytics

Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.

The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.

Read More: Silly mistakes that can cost ‘Big’ in Big Data Analytics

#top big data analytics companies #best big data service providers #big data for business #big data technology #big data mistakes #big data analytics