Data Engineering Challenges – Key Trends

Data Engineering is one of the key components in the field of Data Science. Professionals who are trying to make a career transition into the field of Data Science often ignore these key part and focus more on predictive analytics. However, it is the Data Engineer who lays out the data in a flexible format to perform future analysis.

In this article, you would learn about Data Engineering in general and the various key trends

How Data engineering has been evolving since last few years and what are the current challenges

Previously, Data Engineering dealt with creating data pipelines using Structured Query Language (SQL) or performing some ETL operations in the data warehouse. However, the evolution over the years has called for more advanced skills such as Backend Data Engineers or Data Engineers with a software development background.

The emergence of Big Data resulted into the development of Hadoop and its associated frameworks. Hadoop facilitates parallel computing which not only helps in processing huge volumes of data but also enables faster computation. The data is stored in the Hadoop Distributed file system and processed via the Map Reduce jobs. To ease the process of performing Map Reduce operations Facebook introduced Hive which allows users to write SQL-like queries and execute Map Reduce operations in the backend.

Some of the challenges that Data Engineers are still facing are –

Data Quality Issues – The amount of data that’s getting generated these days from a plethora of sources are unclean and carries a lot of inconsistency. Thus it’s a challenge for a Data Engineer to clean and provide relevant data to the Data Scientist.

Data Pipelines Testing – This is a tricky prospect and a leak could incur a massive loss to the business.

Context Switching – Often it would take a long time to run an ETL job and errors are bound to occur during the run time. Thus it is a challenge to get back into the mindset and run the next iteration.

Alignment – Data Engineers are the pillars of any Data Science project and thus problems could arise due to inconsistent data. In a large organization, it is necessary to build consistency and alignment.

How some of the data engineering tools/platforms are positioned in the market

Some of the Data Engineering tools which are relevant

Python – An open-source language which has a huge community and provides a lot of flexibility in writing clean code. For Map Reduce operations, Python could be used alongside Java.

Apache Hadoop – Needless to say, the Hadoop framework has been the masterpiece when it comes to dealing with Big Data. The plethora of components starting from HDFS to Hive allows to store both structured and unstructured data and also process by writing Map Reduce jobs or simple SQL-like queries.

“Data is the new science. Big Data holds the answers.” – By Pat Gelsinger

Apache Spark – Unlike Hadoop, Spark allows real time data processing which is makes it faster and more go to tool in the current market. It is almost hundred times faster than Map reduce.

Apache Kafka – Kafka is a messaging platform which supports automatic recovery and is resilient to node failures. It has the fault tolerant storage ability and builds data streaming pipelines to facilitate data transfer between applications in real time.

What are the pros of some of the tools in Data engineering space?

Apart from the tools whose advantages we have already mentioned, there are few other tools which are important in Data Engineering.

SQL – SQL queries allows to fetch results at a rapid speed without the need of any coding experience. It is portable on all devices and has a well-defined standards. You could directly communicate with the database using the SQL language and provides a facility to view a database in multiple forms.

Scala – It is the go-to programming language for Spark. Scala is built on top of JVM and is compatible with Java. It is interpretable as well and supports various string operations. Software code written in Scala is easier to debug and deploy. It supports object oriented programming and consists of a full-featured API library.

AWS – Amazon Web Services are globally scalable and mitigates cost. Its EC2 instance provides extreme data manipulation flexibility to the companies.

Azure – The Azure service provide scalability, security, and also helps in disaster recovery. It also ensure cost savings.

What are the key trends in data engineering?

Data Engineering requires working with either Python or Java, Hadoop or Spark, and so on. Some of the trends in Data Engineering are –

Log Accumulation and Analysis using Kafka and Spark. The real time logs data in any e-commerce industry could be injected via the Kafka messaging system into the Spark framework to be analysed and take relevant actions in real time.

Building a Machine Learning model using the data collected via Kafka and with the use of the Spark’s MLlib library.

Text Sentiment Analysis by storing the Twitter data using Kafka on the Hadoop storage system.

How Taliun is contributing in solving data engineering problems

Taliun has been working very closely with different data engineering teams from ISVs and enterprises to solve the digital data engineering challenge with a solution that is based on no code and self-service approach.

The solution approach focuses on sourcing structured or unstructured data from different sources, aggregating it and then building the actionable reports that can be embedded in any application or device. The platform is currently native to AWS and can be deployed on customers AWS accounts, where any business users can easily leverage the historical data to build charts, metrics, dashboards, reports, visualization or Advance AI. Also since they’re built as web apps, sharing and embedding them is easy to integrate and distribute.

About Taliun

Taliun is a silicon valley company, which brings rich domain experience and technology expertise to power the world of data driven decisions, especially for healthcare. Be it building a comprehensive patient engagement strategy using technology or building a continuum of care ecosystem towards population health management initiative, Taliun helps enterprises build meaningful technology solutions through a data sourcing, engineering and aggregation approach.

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