Data Observability: The Next Frontier of Data Engineering

To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools, but also technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering and a pillar of the emerging Data Reliability category.

As companies become increasingly data driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshescloud warehousesdata visualization tools, and data modeling solutions), the mechanics behind data quality and integrity has lagged.

No matter how advanced your analytics dashboard is or how heavily you invest in the cloud, your best laid plans are all for naught if the data it ingests, transforms, and pushes to downstream isn’t reliable. In other words, “garbage in” is “garbage out.”

Before we address what Data Reliability looks like, let’s address how unreliable, “garbage” data is created in the first place.

#data #data-engineering #programming #data-quality #data-science

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Data Observability: The Next Frontier of Data Engineering
Siphiwe  Nair

Siphiwe Nair

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

Data Observability: The Next Frontier of Data Engineering

To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools, but also technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering and a pillar of the emerging Data Reliability category.

As companies become increasingly data driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshescloud warehousesdata visualization tools, and data modeling solutions), the mechanics behind data quality and integrity has lagged.

No matter how advanced your analytics dashboard is or how heavily you invest in the cloud, your best laid plans are all for naught if the data it ingests, transforms, and pushes to downstream isn’t reliable. In other words, “garbage in” is “garbage out.”

Before we address what Data Reliability looks like, let’s address how unreliable, “garbage” data is created in the first place.

#data #data-engineering #programming #data-quality #data-science

Uriah  Dietrich

Uriah Dietrich

1618137000

Data Observability: How to Fix Data Quality at Scale

Companies spend upwards of $15 million annually tackling data downtime, in other words, periods of time where data is missing, broken, or otherwise erroneous, and 1 in 5 companies have lost a customer due to incomplete or inaccurate data.
Fortunately, there’s hope in the next frontier of data: observability. Here’s how data engineers and BI analysts at Yotpo, a global eCommerce company, increases cost savings, collaboration, and productivity with data observability at scale.
Yotpo works with eCommerce companies across the world to help them accelerate online revenue growth through reviews, visual marketing, loyalty and referral programs, and SMS marketing.
For Yoav Kamin, Director of Business Performance, and Doron Porat, Data Engineering Team Leader, having consistently accurate and reliable data is foundational to the success of this mission.

#data-analysis #data-observability #data-engineering #data-quality #data

Siphiwe  Nair

Siphiwe Nair

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