Data engineers face numerous challenges in managing large datasets, maintaining quality control, and handling complex workflows, which can impede productivity. “Almost 70-80%of the work involves data preparation, engineering, and standardisation. It’s all manual work, and frankly, the most painful activity,” said DataSwitch chief Karthikeyan Viswanathan in an exclusive interview with AIM.
Handling data from diverse sources presents a major obstacle for engineers managing multiple-source extractions. Beyond collection, ensuring quality and consistency across these varied inputs remains critical, as poor data can lead to flawed insights and decisions.
However, the task is easier said than done. Merging data from different sources in varied formats and schemas is a labour-intensive job often involving custom coding and manual scripting.
A frequent gripe is that data engineers spend much of their time buried in code and debugging, leaving little room for true innovation. Therefore, as data volumes continue to surge, scalable and efficient data pipelines become essential.
“Data engineering is more than just writing pipelines with SQL and Python. It’s about solving business problems and delighting end-users,” said Zach Morris Wilson, the founder of Dataexpert.io. He added that data engineers who understand the business they’re working in have a significant advantage because they know when to say no to low-value requests.
A data engineering professional on X pointed out, “We have a data engineering problem. AI keeps getting better, but the inputs needed are trash or hard to get. The big money is going to be in securing the best quality data possible.”
Today, every enterprise wants to use AI, but most data isn’t ready for it. This disconnect between AI aspirations and data readiness can result in failed projects, wasted resources, and missed opportunities.
Several challenges hinder enterprise AI readiness, including data silos, quality issues, lack of governance, integration difficulties, and the overwhelming volume and velocity of data generated daily.
To bridge the gap between current data states and AI readiness, enterprises should focus on establishing an AI vision and data strategy, implementing robust data governance, improving data quality, developing scalable infrastructure, prioritising data integration, adopting DataOps practices, and investing in data engineering talent.
Interestingly, a Gartner report highlights that synthetic data generated with generative AI could reduce the volume of real data needed for machine learning by half by 2024. The report recommends building AI into all capabilities, including data ingestion, data quality, cost monitoring, insight generation, and sharing, to address bottlenecks and accelerate data and analytics pipelines.
DS Integrate to the Rescue
This is where DS Integrate comes into play. It offers a user-friendly interface with pre-built connectors and functionalities, enabling businesses to ingest data from various sources and transform it into a usable format without extensive coding expertise.
Its toolkit supports various data formats, including structured and unstructured data, from sources such as PDFs, images, and text files. By automatically generating code for data catalogue creation, DS Integrate greatly minimises the manual effort needed for data preparation.
“With no code, DS Integrate will reduce the dependency on core technology personnel, allowing business professionals themselves to perform data analysis. That is one of the objectives; it’s not that DataSwitch is killing jobs,” said Viswanathan.
He stressed that to get the most out of AI, the data must be well-prepared. According to him, this means prioritising both “AI for data” and “data for AI” and making the processes more self-serviceable. “We want to make our customers’ data ready, standardised, and prepared for AI use, as this is a common challenge every enterprise faces,” he said.
DS Integrate automatically generates code to create a knowledge base in a format compatible with cloud databases such as Spark, Talend, Matillion, DataBricks, and more.
Also, after standardising data, DS Integrate enables users to convert raw data into valuable insights without requiring advanced coding skills. This approach, termed Citizen Data Engineering, dramatically improves accessibility to data engineering and encourages innovation and agility, facilitating quick adaptation to changing market dynamics.
Even though data engineering is a tough job, it is highly sought after. According to AIM Research, 10,593 job openings for data engineers across industries were listed on online job portals.
The post DataSwitch’s DS Integrate Makes Life Easier for Data Engineers appeared first on Analytics India Magazine.
