Why You Need Data Engineers In Your Big Data Projects?
Generally speaking, data science is a multidisciplinary field that applies algorithms, frameworks, scientific approaches, and procedures, to generate insight from data and a plethora of applications. And data is one of the most important features of all businesses as it allows businesses to recognize customers’ needs in an innovative way, and then make considered decisions based on facts, trends, and statistical numbers. Customers are the foundation of any business success. Data science connects businesses with their customers in a modified way to assert better product quality and strengths.
All data management practices are no longer just one database administrator’s job. In the past 10 years, databases have moved to the cloud and achieved unprecedented performance and complexity. Databases have evolved to data warehouses and data lakes. Therefore, the role of the database administrator has changed significantly. To fulfill big data best practices, big data projects now need data engineers, data analysts, and data scientists.
These roles are very different. Let’s find out what these roles are all about!
What Is The Difference Between Data Engineer vs Data Scientist vs Data Analyst
Data engineers, data scientists, data analysts are sometimes used interchangeably, causing many people to mistake they are just different names for the same role. These three roles involve a variety of skills and responsibilities and play a key aspect in processing data sets and shaping your data strategy.
Data engineers are responsible for building, testing, and maintaining a data ecosystem. This data ecosystem is critical for companies and for data scientists who analyze data to build predictive algorithms. Data engineers lay the ground for data analysts and scientists to generate new insights. The data architecture, which is prepared by data engineers can be used for data ingestion and storage, algorithm creation, deployment of ML models and algorithms, and data visualization.
Both data analysts and data scientists work with data. The only difference between the roles of them is the scope of work: data scientists work with a range of complex data while data analysts usually work with numeric data.
Data analysts are responsible for finding answers to business questions by generating ad-hoc and regular reports based on the existing data. Data analysts also collect data, familiarize with the types of data, how it can be sorted, process data to make sure data is error-free, interpret data and analyze how it solves a business problem. This role is suitable for those interested in data-related careers.
Data scientists are responsible for cleansing and collecting high-quality data, identifying hidden patterns, building ML models, refining business metrics, and in charge of data visualization.
Now that we understand all roles in the data engineering field, let’s take a closer look at the data engineering practices and why it is important for any businesses that are running a big data project.
Why Data Engineers Are Needed In Big Data Projects?
It is predicted that the global big data and data engineering market will reach $77.5 billion by 2023 (per report by ResearchAndMarkets.com). With the proliferation of intelligent platforms like frequency trading systems or global e-Commerce platforms, we need big data analysis systems that can handle a massive amount of data.
However, these systems are not only for large companies, even the small businesses can consume large amounts of data from users, sensor arrays, external systems, etc nowadays. As businesses grow and the number of sources and types of data increases, processing these streams without any delay or data loss is a challenge.
Without data engineering and data engineers, you can’t execute big data initiatives and strategies in their entirety: no data equals no data analysis or science, fragmented data leads to inaccurate measurements, poor-quality predictions, and erroneous information, and delayed data leads to inaccurate and untimely decisions.
Data Engineers’ Role in Data Lakes And Data Warehouse
Data warehouse and data lakes are two different approaches to storing and using data.
A data warehouse is a conventional way to store data because data is stored in centralized enterprise repositories that are primarily used for data reporting and analysis. New data is kept by a source system that works strictly according to predefined ETL schemas and rules, and giving you quick access to your structured historical data on different levels.
Data lakes, on the other hand, store unstructured, raw form of data in scalable cloud storage. Using the Schema-on-Read approach, data lakes are flexible for any users or systems accessing your data because the knowledge of existing database schema is no longer required.
Also, since data is stored in its original format, no transformation/conversion is required, making the job of data analysts and data scientists easier. Additionally, cloud repositories are completely separate from the computing resources, so users with serious storage needs can balance their spending and avoid paying for CPU time that they don’t immediately need.
Both data storages are vitally important; therefore, they cannot be neglected. They complement each other. Data lakes are used in the staging and processing layers while data warehouses are served as a compliance environment that shows you how your data would be exposed to the business users. In simpler words, data lakes are like a technical solution and a data warehouse is a business solution.
Building and designing a functional data warehouse and data lake can be a complex task and data engineers are the experts that businesses look for.
The increasing reliance on big data is changing the way companies make decisions, deliver services, and respond to market demands. Data engineers, a key element of your big data strategy, is a very important area that cannot be underestimated. There is no doubt that as the complexity of data processing systems increase, there will be more and more solutions to simplify ETL operations and solve the most difficult data engineering problem.
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