As an organization scales, the number of data consumers within the org also grows, and that brings complexity in terms of how they want to transform and consume data. For example, a data engineering team wants to consume raw unstructured data like PDF files and images to build AI capabilities, whereas the RevOps team needs to build reports leveraging well structured data from enterprise resource planning (ERP) and various other systems to better understand the state of the business.

As more and more lines of business adapt to data-driven decision making and infuse AI to improve operational efficiency, the need for diverse data workloads will continue to grow. 

Diverse data workload challenges

There are several challenges that come with having diverse data workloads. Here are a few examples: 

• Managing multiple data ingestion processes for each workload leads to increased complexity and maintenance overhead.
• If you use structured and unstructured data, it can be challenging to store data at two different destinations. Not having a unified storage layer leads to increased data redundancy and isn’t cost effective. 
• Data governance can become difficult, and data silos can occur.

The benefits of a data lakehouse

In today’s data-driven world, organizations are constantly striving to derive actionable insights from their data. Traditional data warehouses are excellent for structured data but fall short when handling the diverse data types prevalent in modern enterprises. On the other hand, data lakes offer flexibility but often lack the performance and management capabilities needed for efficient data analytics.

Enter the data lakehouse–a new architectural pattern that combines the best of both worlds where storage and compute layers are separated. 

In the data lakehouse pattern, the data is first ingested to the data lake from all the sources. You can then use any best of breed query engine tool to generate analytical reports from the available data in the data lake or use it to train your AI/ML models. Now, you have a single source of truth for your data that can serve multiple data workloads efficiently.

You can use Celigo’s iPaaS (Integration Platform as a Service) to automate ingestion from diverse sources and execute transformation steps. By leveraging a data lakehouse and iPaaS, you can see a range of benefits, including:

• Simplified data ingestion: Since you just have to build and manage ingestion to the data lake, as opposed to building and managing them separately for each workload, the data ingestion process streamlined.  
• Significantly reduced storage costs: For example, Amazon S3 standard object storage offers a low price of $0.023 per GB for the first 50 TB/month.  In a data lakehouse, data can be tiered based on its importance and frequency of access. Frequently accessed data might be stored on slightly more expensive, faster storage tiers, while less critical or older data can be stored on cheaper, slower tiers. This tiering approach leads to further cost savings.
• A single source of truth: Data lakes serve as a single source of truth and repository of all the data in your enterprise. This helps to enforce data quality standards at scale, which is crucial to prevent the data swamp problems associated with the data lakes. 
• Agility and flexibility: Data consumers can build their own transformation workloads with their choice of tools.

Conclusion

Celigo’s iPaaS not only streamlines data ingestion and transformation processes, but also empowers businesses to leverage their data effectively, which leads to improved customer experiences, operational efficiencies, and a competitive advantage in the market. With Celigo, you can ingest data from 400+ applications and sources to data lakes, such as S3, Azure ADLS, GCP storage, and more. You can also automate transformation steps with Celigo’s iPaaS that you wish to execute on Snowflake, Databricks, or your choice of query engine.

To dive deeper into this topic, register for our webinar on October 9th: Modern data stack: Data Lakehouse Pattern and Snowflake. And, check out this ebook, “The modern data stack” to learn more about enabling data-driven decisions and agility at your business.