Why is big data engineering a valuable skill to have?

Here, we list some of the important skills that one should possess to build a successful career in big data.

  • Database tools    

A thorough understanding of database design and architecture is essential for data engineering job tasks because they frequently include storing, managing, and organising large volumes of data. Structure query language (SQL)–based and NoSQL–based databases are the two varieties that are most frequently utilised. Structured data is typically stored in SQL–based databases like MySQL and /SQL, although NoSQL technologies like Cassandra, MongoDB, and others may also store substantial amounts of structured, semi–structured, and unstructured data depending on the needs of the application.

  • Data transformation tools 

Big data is available in a raw state that cannot be utilised immediately. Depending on the use case, it needs to be processed before being translated into a consumable format. Depending on the data sources, formats, and desired output, data transformation can be straightforward or difficult. Hevo Data, Matillion, Talend, Pentaho Data Integration, InfoSphere DataStage, and other software are examples of data transformation technologies.

  • Data ingestion tools

One of the key components of big data abilities is the ability to move data from one or more sources to a location where it can be analysed, which is known as data intake. Data ingestion becomes increasingly difficult as both the volume and variety of the data grows, necessitating the knowledge of data ingestion tools and APIs to prioritise, validate, and dispatch data in order to assure a successful ingestion process. You should be familiar with tools for data ingestion such as Apache Kafka, Apache Storm, Apache Flume, Apache Sqoop, Wavefront, and more.

  • Data mining tools

Data mining, which entails removing key information from huge data sets to uncover patterns and prepare them for study, is another crucial ability for handling big data. The classification and prediction of data are made easier with the use of data mining. Apache Mahout, KNIME, Rapid Miner, Weka, and other data mining tools are a few that big data specialists need to be familiar with.

  • Data warehousing and ETL tools 

Businesses may effectively use big data with the aid of data warehouse and ETL. It streamlines data from various sources that are diverse. ETL, or Extract Transform Load, gathers information from many sources, transforms it for analysis, and then loads it into a warehouse. Talend, Informatica PowerCenter, AWS Glue, Stitch, and others are a few of the well–known ETL tools.

  • Real–time processing frameworks

Real–time data processing is necessary to produce immediate insights that can be put to use. The most common application for Apache Spark is as a distributed real–time processing framework for data processing. Hadoop, Apache Storm, Flink, and other frameworks are a few that you should be familiar with.

  • Data buffering tools

Data buffering has emerged as a key factor in the acceleration of data processing power as a result of rising data quantities. A data buffer is essentially a location where data is temporarily stored while being transferred from one location to another. When streaming data is regularly generated from thousands of data sources, data buffering becomes crucial. Data buffering solutions like Kinesis, Redis Cache, GCP Pub/Sub, etc. are frequently utilised.

  • Machine Learning skills

By identifying trends and patterns, machine learning integration helps speed up the processing of huge data. It is possible to classify incoming data using machine learning algorithms, spot trends, and turn data into insights. A solid background in statistics and mathematics is necessary to understand machine learning. These abilities can be developed with the use of tools like SAS, SPSS, R, etc.

  • Cloud computing tools

One of the main duties of big data teams is to set up the cloud to store and guarantee the high availability of data. So, learning it becomes a necessity if you want to work with large data. Depending on the amount of data that needs to be stored, businesses choose hybrid, public, or internal cloud infrastructure. AWS, Azure, GCP, OpenStack, and other well–known cloud computing platforms are only a few.

  • Data visualization skills

Visualization tools are used extensively by big data specialists. The insights and lessons developed must be presented in a way that the end users may easily consume them. Tableau, Qlik, Tibco Spotfire, Plotly, and other commonly used visualization software are among the tools that can be mastered. The best approach to develop these data engineering abilities is to obtain certifications, gain practical experience, and experiment with new data sets by integrating them into actual use cases. Success in mastering them!

The Data Engineer Learning Path

the technical data engineer learning path is as follows:

• Hone your programming skills in languages like Python and Scala.
• Learn scripting and automation.
• Learn database administration and hone your SQL abilities.
• advanced data processing methods.
• Learn to plan out your processes.
• Expand your knowledge of cloud computing with AWS and other platforms.
• Learn more about infrastructure tools like Docker and Kubernetes.
• Keep abreast of business developments.

A wonderful career option for aspirant data professionals is data engineering one of the most in-demand jobs in the data science industry. We strongly advise you to pursue our career track Data Engineer with Python if you are committed to work in the field of data engineering but are unsure of where to begin. This track will provide you with the theoretical and practical knowledge required to succeed in this field.