Data Engineering

Data engineering is the process of creating systems that make it possible to collect and use data. Typically, this data is utilized to support later analysis and data science, which frequently includes machine learning.


Tap the power of data coming from multiple sources for effective business analysis with CrossAsyst!

Our data engineers at CrossAsyst plan and build pipelines that transform and transport data into a format that is highly useful when it is received by data scientists or other end users. We employ a unique practice of designing and building systems for efficiently storing, collecting, and analyzing data at scale. While our methodologies appear to be quite straightforward, our developers possess multiple data literacy abilities to achieve the desired outcome.

Focus Areas

However, data engineering is not something that everyone can sail through. There are some of the most important skill areas required to ace such a profession. These primarily include :

Foundation software engineering

Distributed systems include software engineer skills and software architect skills. Open Frameworks include Apache Spark, Hadoop, perhaps Hive, MapReduce, Kafka, and others.


Programming –The preferred language for handling data is now Python. While still in demand, Java has lost favor with the majority of data scientists and engineers. Another language on which Apache Spark and Kafka are built is Scala.

Cloud Platforms

Microsoft Azure and Google Cloud Data Engineering are close behind. Leadership teams may experience poor decision-making and a slowdown in data-driven innovation as a result of incorrect data or delayed insights. The challenges posed by subpar data engineering procedures in today’s data-driven businesses run the risk of raising tensions and encouraging unsafe solutions.


They are necessary for being able to properly manipulate the data so that it is in a form that is accessible for those performing the final analysis on it.

Data Modeling

It helps data engineers create tables, and partitions, decide where to normalize and denormalize data in the warehouse, and think through how to retrieve specific attributes.


The data from this year revealed some fascinating information regarding the level of development of different DataOps and engineering processes, the popularity of various cloud data platforms and technologies, and the main difficulties that data engineers currently face.

Data leaders and practitioners can gain from understanding how these trends may affect their decisions about human and technology resource allocation as data use continues to advance quickly. Here is a sneak preview of the main challenges we found. Watch this space for further articles outlining the leading cloud data platforms that data-driven enterprises are implementing, as well as the reasons that multiplatform investment is growing.

Stay ahead of the curve and strengthen your data engineering team with CrossAsyst!