Data is everywhere, and everything is data.
You may not even realize this, but every single day there are massive amounts of data generated just around you. Right from your smartphone, social media profiles, your Teams meetings, to your Wi-Fi connected fridge that lets you know that you are out of milk.
The data generated worldwide is growing at an accelerated pace. From 2018 to 2020, the amount of data generated has grown from 33 to 64 Zettabytes (ZB). This is a 41% average annual growth.
Moreover, it is estimated that by the year 2025 the world will have created and stored over 180 ZB of data.
If you wish to visualize these numbers, one ZB is 8,000,000,000,000,000,000,000 bits. Let’s say that each bit is a sand grain. It means that one ZB of sand grain would be enough to supply all the beaches and deserts on Earth*.
Storing data in various data systems itself is already challenging. Nonetheless, the real difficult part is tapping into the value from this massive amount of data.
And that is the main focus of Big Data.
Big Data is growing
The Big Data market is continuously growing along with the growing demand for aspiring Data Engineers, Data Scientists and other data professionals from businesses. From 2020 to 2022, total enterprise data volume has grown from 1 to 2,02 Petabytes (PB). It translates into 42% average annual growth, similarly to average annual total growth of data generated worldwide.
By tapping into the value from data, companies are capable of making better-informed decisions, increasing their enterprises’ protection, helping their businesses thrive, and outperforming the competition.
Nevertheless, to do all of the above, companies need to dispose of employees with specific skill sets. That’s why business need to hire Data Engineers, Data Scientists, and Machine Learning Engineers.
In this article you will learn:
- What is Data Engineering?
- What does a Data Engineer do?
- Data Engineer vs. Data Scientist – is this the same?
- Why is Data Engineering important?
- How Data Engineering adds value?
What is Data Engineering?
In brief, Data Engineering is making raw data, coming from various sources, usable to Data Scientists, Data Analysts, and other groups in organizations. However, to increase data usability, a Data Engineer need to take into consideration many aspects.
First of all, businesses generate and store data that usually has compliance requirements that are legally required to be protected. Consequently, security of data plays a key role and brings technical challenges for data in transit and at rest.
Apart from being secure, data in the company must be:
- available to end-users,
- meaningful for business requirements,
Consequently, data governance strategies in enterprises require specialized skills, which makes the role of a Data Engineer crucial for modern businesses.
What does a Data Engineer do?
Data Engineer is a profession that combines multiple roles and responsibilities. As a result, the exact scope of the role is highly dependable on an organization’s needs.
Generally speaking, the role of a Data Engineer is to store, extract, transform, load, aggregate, and validate data. It can entail:
- creation of data pipelines and data storage for analytical tools that query the data,
- data analysis with accordance to governance rules and regulations,
- understanding the advantages and drawbacks of certain data storage (for example relational database systems or data lakes) and query options.
For example, let’s assume that an organization uses the services of Oracle as a cloud provider. The company needs to store and query data from multiple data sources. To choose the best solution the Data Engineers must take into consideration:
- data structure,
- normalization of data,
- fact of whether data is key or value-based,
- what the relationships are within the data,
and act accordingly.
Data Engineer vs. Data Scientist – is this the same?
In spite of the fact that companies have already understood the painstaking need for Big Data implementation overwhelming the job market with job offers to hire a Data Engineers or a Machine Learning Engineer, data jobs are still subject to misconceptions.
For many of them, Data Engineers, Data Scientists, and Machine Learning Engineers, MLOps are just blurry technical positions that mean all the same.
Data Science hierarchy of needs, source: https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007, created by Monica Rogati
Let’s look at the Data Science hierarchy of needs created by Monica Rogati. As you can see, the data process has a certain hierarchy that needs to be adopted.
Whereas Data Engineer’s job starts from the bottom from the collect phase and includes the move/store phase, Data Scientist’s job starts with explore/transform and extends almost to the top, leaving the very tip of the pyramid for Machine Learning Engineers.
Summing up, Data Engineers and Data Scientists are complementary professions, but they are not the same. Data Engineer’s focus is to design, build, and arrange data pipelines. On the other hand, a Data Scientist uses pipelines to analyze, test, create, and present data in the optimal way.
If you want to read more about Data Engineers vs. Data Scientists, you can check out our article – Data Engineering vs Data Science – what is the difference?.
Why is Data Engineering important?
As already stated, a Data Engineer profession is essential for companies of all sizes to answer critical business questions based on insights coming from data. Data Engineering is designed in a way to support the process of reliably, efficiently and securely inspecting all the data available.
With the increasing amounts of data generated in enterprises, Data Engineering plays a vital role in supporting modern Data Analytics and Data Science teams.
In the past, Data Engineers focused on creating data warehouse schemas to quickly process queries and ensure high performance.
Nowadays, organizations frequently use centralized repositories, such as data lakes. Consequently, Data Engineers have more data to manage and to deliver for Data Analytics. There, there is usually unformatted and unstructured data, which needs recognition and action from Data Engineers.
How Data Engineering adds value?
To put it briefly, Data Engineers add value by automating, optimizing complex systems and transforming data into usable and accessible business assets.
ELT and ETL processes
As already mentioned, data, especially in data lakes, comes in different flavors. It is then up to Data Engineers to decide what strategy they should adopt and why.
The two most common ones for extracting, loading and transforming data are ELT (extract, load, transform), and ETL (extract, transform, load) strategies.
ELT in Data Engineering
The ELT is usually used by Data Engineers within data lake architectures or systems that are in need of raw extracted data from multiple data sources. It allows various systems to process data from the same extractions.
Therefore, if data is combined from various systems and sources, it can turn out to be beneficial to co-locate and store it before making any transformations in data processing systems.
It is also worth mentioning that ELT is usually ELT-L, as transformed data is usually loaded into other locations for end-consumers, such as Hadoop, or Snowflake.
ETL in Data Engineering
On the other hand, Data Engineers also use ETL processes that rely on heavy computation to transform data before loading results into a database, a file system, or a data warehouse.
Usually, this strategy is less performant than ELT processes as data for every stream is often required from related systems. Consequently, every execution requires to requery data from certain systems, add extra load, and wait for the it to be available.
The common recommendation is to use ELT processes to increase data quality, performance, availability, and enablement. Nevertheless, ETL processes can be appropriate when simple transformations are applied to a single source of data.
High performance of data
Apart from data correctness and availability, data must also perform. When large amounts of data are processed, special processes and checks are needed to ensure that data meets service level agreements (SLAs) and adds value to the business. All of these also need to be ensured by a Data Engineer.
Nevertheless, it is crucial to define what high performance of data means. Knowing this, Data Engineers must take into consideration:
- the frequency of receiving new data,
- the time that transformations take to run,
- the time it takes to update the target destination of their data,
and ensure that data is performant. It will allow business units to find valuable insights for the company whenever they need to.
Currently, data governance requirements, best practices, security procedures, or business requirements are constantly changing, so the production environment should also change accordingly.
Consequently, deployments must be automated and verifiable. Data Engineers make sure that there are automated processes that verify that code works as expected in different scenarios using unit and integration testing.
Unit testing, which is an integral element of the Data Engineering skills, verifies that individual pieces of code generate expected outputs, whereas integration testing ensures that pieces of code work together and provides expected outputs for a given series of inputs.
Providing value to customers quickly is crucial for businesses. Nevertheless, equally important is to have a solid action plan in case of system failures.
Numerous enterprises expect that these are cloud providers that minimize the risk of downtime and guarantee SLAs. Unfortunately, failures usually happen at some point. Therefore, systems must be designed in a way to tolerate critical system failures.
In the case of disaster recovery scenarios, companies need to define standards to understand the impact on their customers and the time it will take to make systems available again.
When it comes to data, Data Engineers are also responsible for creating processes to make sure that data pipelines, databases and data warehouses are compliant with these disaster recovery standards.
Along with an increasing reliance on IT systems and cloud computing, potential threats to the data stored in companies have arisen. Data can be lost due to system failures, corrupted by a virus, removed, or modified by hackers.
Consequently, the role that Data Engineers play in providing Data Security has come to the forefront. As a result, a good Data Engineer must be involved in making sure the data processed is appropriately protected by:
- identifying unsafe data access or practices in pipelines that could lead to information leak or policy violation.
- monitoring, logging, and tracking access to data repositories, databases, machines, containers, code, and processing systems to ensure that only authorized people have access,
- validating the protection of sensitive data,
- performing security tests as part of the tests and development cycle.
Summing up, the process of turning data into valuable insights for businesses can be highly complex and there are many different levels of data processing and analysis. Throughout this article, we have talked about:
- Data Engineering and its importance,
- Data Engineers and Data Scientists’ focus,
- Data Engineering skills,
and how each of these are placed within the Big Data ecosystem.
Truth be told, knowledge is power, and knowledge comes from data. Currently, businesses create, ingest, and process such amounts of data that investing in data professionals, like Data Engineers, Data Scientists or Data Analysts, is crucial to making their businesses thrive and preserving competitive advantage in the market.
* We would even have an excess of sand grains as, according to University of Hawaii 7,500,000,000,000,000,000 sand grains would be enough – https://www.cosmotography.com/images/m8-m20_desc.html