We have learned a lot about data science and data analytics, but our knowledge in this field will remain incomplete without knowing about the concept of Data Engineering. To be fair, it is enough to say that the work and success of a data scientist or a data analyst depend on that of data engineers. Without data engineering, the whole point of data analysis becomes null and void.
We are living in a largely digitalized world where there is an enormous amount of data transactions happening on a daily basis. The data scientists work on mining and analyzing these data by the application of mathematical and statistical methods. However, before any of these processes can be done, the scientists require a certain data structure because all the data from the various sources are not always in a compatible format. The data obtained from the sources need to be converted into an operable format and that’s precisely where the data engineers come in.
So to explain in simple terms, data engineering can be explained as the aspect of data science course in bangalore which focuses on the real-world implementation of the results and information obtained from data analysis. Likewise, the work of data engineers involves harvesting and applying big data in practical aspects. The main focus of data engineers is to create an effective interface and a mechanism for the smooth access and application of the data.
What is meant by a data pipeline?
As mentioned above, data engineers mostly work with large amounts of data collected from different sources and in heterogeneous formats. The first challenge is to generalize the data type into a single operable format for the smooth flow of information.
After the format of the data is obtained the next task in hand is to build the data mechanism for further analysis of the data. The building of this mechanism is a very complicated procedure as one has to account for the various dependencies among the different data sets and craft the chain of mechanism in such a manner that this order remains the same.
Thus the mechanism of the flow of data for the implementation in real-world systems is known as the data pipeline. This is a very crucial skill for a data engineer as without a data pipeline the data cannot be used for further analysis.
The most vital aspect of data pipelines is that it completely automates the data flow process and eliminates the need for any human or manual intervention. It allows a system to process more than one data stream simultaneously and also provides far more accurate information than manual processing.
Types of Data Pipeline mechanisms
With the increasing popularity of automation, every major firm in the world is making use of data science techniques to utilize the big data which they deal with every day. Hence, it becomes obvious that they will need various data engineering and pipeline solutions. However, the work of every firm is different and so is the type and amount of data collected. Every firm utilizes the data differently according to the one which is most suitable for its business. So it becomes clear that different enterprises will be using different pipeline solutions for their business purposes. Some of the most commonly used pipeline solutions are explained below.
Real-time processing pipeline
Many companies make use of real-time pipelines. These data pipelines are very handy when a firm needs to collect and analyze data in real-time from a market source. This is often utilized by investment firms as they process the data in real-time, which is streamed to them from various financial market sources. This helps in the live processing of the data and continuous updates of the information.
This is the most common technique used by various small scale and large scale companies. The cloud-native pipelines are optimized to collect and process data from various cloud sources. The infrastructure is very simple to create and it works simply from the data mined from cloud sources so the process becomes economically efficient and less tedious. Since these systems are hosted in the cloud the user does not need to worry about maintaining the infrastructure, thereby making it the most popular pipeline solutions.
Many firms need to move a large amount of data at regular intervals for various purposes and not necessarily in real-time. For this purpose, batch processing is very helpful. This provides a solution to the problem of the storage of huge datasets. Many marketing companies make use of batch processing to move large amounts of data into a bigger system for analysis. Batch processing is a common pipeline solution and is mainly utilized by firms and enterprises dealing with large sets of data that need to be moved or integrated into a larger system on a daily basis.
Open Source pipeline solutions
Many small scale businesses and start-up companies often do not have the financial capability to avail of a professional pipeline solution vendor or a hosting service. This problem is solved by open source pipeline solutions which allow the user to avail of its various pipeline building tools and programs at minimal or no cost for building their customized data processing pipeline. However, the user needs to be very well versed in creating data pipelines as they can only access the tools through the open-source, but they have to model the pipeline on their own and that too according to their requirements. Open sources provide the technologies needed for building a pipeline and are open for modification by the user.
Skills required for becoming a full-stack data engineer
Although pipeline development is a vital skill for a data engineer, there are several other concepts that a data engineer must be well versed in to succeed as a professional data engineer. The frameworks created by a data engineer need to work on various aspects so apart from the data pipeline building the individual should have the knowledge about the following topics to ensure smooth functioning of the framework.
Hadoop-based/Big data technologies
A data engineer should be efficient in utilizing various processing tools for analyzing big data structures. Hadoop is the most popularly used data analyzing tool by various data engineers. It is also helpful if the data engineer is an expert in using other systems like MapReduce, PIG & HIVE, Oozie, Zookeeper, Flume, and Scoop.
Data architecture tools
Data architecture mainly deals with building complex database systems. An engineer efficient in data architecture should know how to deal with data in motion, data at rest and the various dependencies and interdependencies among the different data sets present in a system. This knowledge is necessary to model the pipeline for data processing.
In-depth knowledge about Database
A data engineer should be well versed in SQL and NoSQL as they are vital for developing a database. SQL or Structured Query Language is very important for creating the structure, managing and manipulating a database very efficiently. On the other hand, NoSQL is very handy for storing large volumes of structured, unstructured and raw data and for providing access to them as required by the user.
The most challenging job of a data engineer is to aggregate the data collected from various sources. For this purpose, the data engineer should have sufficient knowledge about ETL or Extract Transform Load. It is also known as data warehousing. It is necessary for collecting unstructured data from different sources for analytical purposes.
Coding or Programming
This is the most fundamental skill a data engineer should have. A data engineer should have an expert level of knowledge in any programming languages like Java, Python, C++ or Golang or any other language, but the individual should be highly skilled in that particular language and should have a complete and in-depth understanding of the algorithms employed by various programming languages.
Different OS knowledge
A data engineer needs to be familiar with using different operating systems and not just Windows. Many firms make use of UNIX, Linux and Solaris operating systems because of their efficient compiling abilities for large data sets. The same commands can have vastly different meanings in different operating systems, so the user needs to be familiar with the interface and working of different operating systems.
Although this is not a crucial skill for a data scientist as most of the data engineers work on machine learning as a project assigned to them, knowing machine learning concepts can increase the efficiency of a data engineer exponentially. As machine learning employs various statistical and mathematical interpretation skills on the collected data it is easier for a data engineer to employ those methods for processing raw data.
To conclude this article we can say that being a full-stack data engineer with data pipeline building capabilities is not an easy job. Being a data engineer requires dedication and hard work, but most importantly the individual should have the creative insight needed for making innovations in the various systems.
360DigiTMG – Data Science, Data Scientist Course Training in Bangalore
2nd Floor No, Vijay Mansion, 46, 7th Main Rd, Aswathapa Layout, Kalyan Nagar, Bengaluru, Karnataka 560043