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.

Overview

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.

SQL

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.

Analytics

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.

Highlights

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.

Data quality and validation were the most often mentioned issues in the poll, which is not surprising given that data teams are relying on data more and more, and that data architectures are becoming more complicated.

Poor data quality has often been seen to put BI, data science, and analytics programs in danger right from the start. However, still, various survey participants were unaware of what (if any) data quality solution their firm used. Data quality may not be a priority for data teams in the early phases of developing their data infrastructures and strategies, as evidenced by the fact that this percentage increased to 39% for firms with poor DataOps maturity.

Multiple data teams are extensively required to stay up to date with an ever-increasing number of various data sources, which are quite practically impossible to comprehend easily.

The average daily amount of data generated in 2020 was 2.5 quintillion bytes. By the end of 2025, that amount is anticipated to increase to 463 exabytes, representing an increase in daily data of more than 18,000%. That can be one of the causes of the 80% data underutilized statistic in a business.

Three out of four firms, according to survey respondents, already collect and store sensitive data. This conclusion is consistent with last year’s findings.

Techniques like k-anonymization and differential privacy can be difficult to apply, especially in a variety of cloud systems, which is one of the main issues. It takes a lot of time and resources to manually create and implement privacy rules uniformly across all platforms, and it might also expose users to more danger.

Various dedicated cutting-edge methods are extensively required to assist in protecting as well as de-identifying certain personal data which continue to be elusive.

Even worse, due to ambiguous definitions and standards for anonymization, it might be difficult to ensure that masking and anonymization procedures adhere to the needs of various data use norms and regulations. The most well-known and comprehensive legislation, such as GDPR, HIPAA, CCPA, and SOC 2, were at the top of the long list of rules, but approximately one-third of data teams also had to adhere to internal, company-specific standards.

Organizations of all sizes, in all industries, and in all regions are being impacted by the development of data compliance laws and regulations. Eighty-eight percent of survey participants said that at least one regulation applies to their organizations.

Not just data engineers are faced with difficulties by data teams. When DataOps processes are manual or ineffective, it is far more difficult for data platform owners, data architects, and data consumers to complete data initiatives and achieve goals. The requirement to follow a variety of data use mandates places additional pressure on data teams to guarantee those data access policies:

  • Account for differing regulatory guidelines
  • Are implemented consistently across platforms
  • Can be audited on-demand to prove compliance

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

Let's Talk