Untold, state-of-the-art approaches every data engineer must be aware of…

“Learn continually — there’s always ‘one more thing’ to learn

- Steve Jobs

Ever wanted to learn to play the piano or learn a new language? Perhaps you have always wanted to learn to surf or prepare new cuisines?

Learning something new, whatever your passion or age, is not only enjoyable but also beneficial. Similarly, data engineering is evolving at such a rapid pace that it has always something or the other new to learn. It is quite easy and obvious to become overwhelmed by the variety of technologies available.

Modern data engineers and new evolutions in big data

The most productive way to learn these new tools and techniques is to classify these technologies and thoroughly comprehend each one. Let us dig into them:

  • Programming languages
  • Data observability
  • SQL
  • Data pipelines
  • Hyper automation
  • Machine learning
  • Domain-Driven Design
  • Cloud
  • Open Source to SaaS

1. Learning unpopular programming languages

A thorough grasp of programming languages like Python and Java, as well as a detailed understanding of data structures, databases, and business goals, are required for a data engineer to succeed. Python has recently become the most popular language to learn.

Scala is another programming language that is frequently overlooked while discussing data engineering. Though it has recently gained popularity, it does not have the same widespread importance as other popular languages.

Why should big data engineers study Scala?

Zach Wilson, a tech lead at Airbnb, owns a YouTube channel where he explains why studying Scala is crucial and how it may benefit data engineers in their careers. Some vital points covered for Scala are that:

  • Many prominent digital companies, like Netflix and Airbnb, have a strong stake in Scala and develop a lot of pipelines in it, signaling those data engineers who discern Scala will be in high demand.
  • Scala enables data engineers to think like software engineers. You must consider unit testing, continuous integration, integration testing, and other factors when developing a SQL pipeline.
  • Python as a programming language is not very safe compared to Scala. The type-safety feature adds a further layer of security.

If you are a data engineer, you can find sources such as Codecademy, Team Treehouse, freeCodeCamp, Pluralsight, GA Dash to keep up with these recent developments in programming languages and stay ahead of the competition.

2. Power of data observability

Data observability, a new element in the modern data tech stack, gives data teams visibility, automation, and alerts to faulty data (such as fake numbers, data drift, and malfunctioning dashboards). Data observability can also help your company create trust and develop a data-driven culture. When big data engineers employ observability technologies and frameworks, they can completely comprehend where data comes from and how it is used, as well as gain real-time visibility into the condition of known issues. Data engineers can recapture time previously spent putting out fires and dealing with data disasters.

The data engineering team at Blinkist (a German Book Summarizing Service), for example, discovered that automation and control saved up to 20 hours per week per developer. Instead of battling data gone wrong, those important minutes can now be focused on innovation and problem-solving.

3. Supremacy of SQL

Apart from Python, Java, and Scala, it is SQL that is supreme as a domain-specific language. SQL is one of the most crucial skills for a contemporary data engineer to have. If you know SQL well enough, you can save time by writing proper query lambdas, eliminating time inefficiencies in your data model, and using SQL with Grafana to produce complicated graphs that provide useful information about a business.

Today’s most essential data warehouses are all SQL-based, so if you want to be a good data engineering specialist, you will need to know SQL inside and out. You can do this by:

4. Increased complexity in data pipelines

This is one area where data engineers must deal with increasing complexity and bias. A data pipeline is a collection of processes and tools that move data from one system to another for processing and storage. It collects datasets from various sources and stores them in some type of app, database, or tool, giving big data engineers, data scientists, and analysts reliable and quick access to this combined data. Snowflakes Data Cloud solutions and DataBricks Lakehouse are two new names for desperate data lakes. Data warehouse queries, real-time streams, JSON, CSV, and raw data are all frequent.

The skillsets and tools necessary for ETL / ELT pipeline injection may alter depending on how and where data engineers build up storage.

5. Need for hyper-automation

Value-added operations such as running jobs, scheduling, and events are now part of a data engineer’s skill set. This tendency has become increasingly prevalent over the previous ten years, with specific scripting and data pipeline duties necessary to successfully migrate data to the cloud.

For the past two years, hyper-automation has been included in Gartner’s advanced technology trend. This in and of itself is proof that we are living in the hyper-automation era. Hyperautomation is a type of automation that enables jobs and processes to be completed quicker, more efficiently, and with lesser errors. In 2022, data engineers will utilize hyper-automation to combine business intelligence systems, sophisticated business requirements, and human expertise and experience, whilst simple automation will be used to complete simple and repetitive operations that do not require much intelligence.

6. Present correctness in ML

The goal of machine learning is to forecast the future. To train ML models, data professionals require labeled samples from the past, and the condition of the world must be accurately described at the time. If future events seep into training, models will perform well in training but fall short in production.

Data leaking occurs when future data enters the training set. It is significantly more frequent than you might think, and it is tough to diagnose. Here are three frequent blunders for data engineers to avoid:

  • Each label requires its own cutoff time, thus data before that label’s timestamp is ignored. Your training set can include millions of cutoff moments where training and label data must be merged with real-time data. Implementing these connections haphazardly will soon balloon the processing job’s size.
  • All of your features must have a timestamp linked with them so that the model can appropriately reflect the world at the moment of the event. If the user’s profile includes a credit score, it is vital to know how that score has changed over time.

As a data engineer, if you have fittingly evaluated the present correctness in ML, you have unlocked one of the key issues with ML production at your workplace.

7. Domain-driven design is in action

When data producers use DDD and take responsibility for their data products, streaming processing/analytics will benefit greatly from the adoption of data mesh. Data engineers need to tell apart the circumstances that are published from the way they are saved in the functioning source system (i.e. not fringed to customary change data capture [CDC]).

As joins on the row-level are already done, this results in repeated data structures that are considerably easier to process as a stream (compared to CDC on RDBMS, which ends up in tabular data streams that you need to join). This is owing to the decoupling noted above, as well as the usage of value or document stores as the operational persistence layer rather than RDBMS.

8. Concentrate on Cloud

In terms of tooling, all cloud providers have quite a lot in common. The ornate names are meant to confuse you. Concentrate on one cloud provider and hunt up the identical service on another cloud provider online. While there may be some substantial feature differences, even if a data engineer has no prior job experience, they will understand how that tool fits into their end-to-end pipeline.

As suggested by Statista, AWS dominates the market with over 32 percent of the market share. It is always a good bet that you will be able to get your first job.

9. Domain-driven design is in action

When data producers use DDD and take responsibility for their data products, streaming processing/analytics will benefit greatly from the adoption of data mesh. Data engineers need to tell apart the circumstances that are published from the way they are saved in the functioning source system (i.e. not fringed to customary change data capture [CDC]).

As joins on the row-level are already done, this results in repeated data structures that are considerably easier to process as a stream (compared to CDC on RDBMS, which ends up in tabular data streams that you need to join). This is owing to the decoupling noted above, as well as the usage of value or document stores as the operational persistence layer rather than RDBMS.

9. The shift from open source to SaaS

Open-source software has been embraced by several individuals for its communal culture, but why are organizations so enthusiastic about using it? What makes it so great? Convenience is important, but what makes firms use it is its cost-saving capability. All of these criteria now favor SaaS and cloud-native services over open-source software. SaaS suppliers handle everything from infrastructure to upgrades, maintenance, and security. This low-ops serverless paradigm avoids the significant human cost of software management while allowing engineering teams to quickly create high-performing, scalable data-driven apps that satisfy both internal and external customers.

Winding up…

As businesses become more reliant on data to run their operations, it is long past time for data engineers to pay as much attention to data quality as they do to application health. You and your team can create trust, save time, and stop the cycle of late-night emails, last-minute changes, and fire drills by taking a more comprehensive approach to data quality, tools, and discovery, for the better.

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Albert Christopher

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning