The Biggest Data Engineering Challenges of 2022

The dynamic data engineering technology space of today is propelled ahead by the decisive shift from on-premise databases and BI tools to modern and advanced cloud-based data platforms built on lakehouse architecture.

Today’s challenging data environment stipulates, reliance on multiple technologies to keep up with scale, speed and use cases. The cloud data technology market is advancing swiftly, and includes an extensive set of open source and commercial data technologies, tools, and products, unlike the on-premise data warehouses of the past.

Organisations are developing DataOps Frameworks and Functions to be maximize the value of their data and to be in relevance. To enable automated and continuous delivery of data to business intelligence analytics and data powered products, processes and DataOps tools are in place.

As per a recent study by Gradient Flow and Immuta, respondents cited these areas as the most challenging in the data engineering space:

• Data Quality and Validation;
• Monitoring and Auditing for Compliance;
• Masking and Anonymization; and
• Data Discovery.

We at Ignitho have been building capabilities, across our Data Engineering Practice to mitigate these challenges in the most holistic way possible.

With potential sources of error increasing day by day; factors like volume, variety, velocity, data source type, and the number of data providers, are playing a huge role in how we face the concern of data quality. Solutions based on our Informatica and TensorFlow Data Validation capabilities have helped our clients tackle data quality issues and challenges impacting critical and products, depending on the accuracy of Data.

At times, when organizations’ have to handle myriad of data sources and data types, such as unstructured data consisting of text, images, audio, and video; the data integration solutions with Apache Spark, dbt and Hive as well as managed services like AWS Glue, Dataform and Azure Data Factory has been adding value to our clientele’s data compliance efforts.

Data Masking and Data Encryption are distinct data privacy solutions. Ignitho’s regulated approach on GDPR, HIPAA, CCPA, and SOC 2 has been pivotal in removing the misconception among data stewards on Data Governance and Data Privacy when considering data anonymization solutions, that encrypted data is indeed a form of data masking.

A recent study indicated that close to 1 in every 4 organizations, do not have a structured Data Catalog or Discovery tool. As they grow, the amount of raw and derived data they generate and store rises, and users need tools to help them discover the right data resources. Ignitho’s expertise in the areas of Google Data Catalog, Collibra, and Azure Data Catalog, are assisting organizations drive adoptability.

Headquartered in the US, Ignitho services its customers through its offices in London, UK, and New York and Richmond, USA, and its development centers in Brighton, UK and Kochi, India. We are rapidly gaining recognition as a market leader in Frugal Technology Innovation for Enterprises – the ability to do more with less.