Data Integration on Cloud is increasingly witnessing a change in approach from the traditional ETL (Extract, Transform & Load) process to the modern ELT (Extract, Load & Transform) process. This change is due to the rising adoption of an underlying cloud data lake based architecture that feeds into the downstream cloud data warehouse/datamarts. Let’s take a closer look at why organizations are moving towards cloud-based data platforms?
Current Scenario Vs Actual Need
At the highest level, organizations today need to manage the ever growing data and know how to extract valuable insights from data. There is a huge gap between the actual value extracted and the potential value that can be extracted from data. Data silos and stand-alone analytics systems make up for the common reality that many organizations are trying to overcome to extract tangible value from data.
The journey to extract value from data typically involves the ability to ingest huge volumes of data, process varied data types & formats, and enable speedy insight generation for business users. Cost is also an important factor to consider.
Enterprises need scalable low-cost storage and flexible processing power with minimal dependency on technical resources. The cloud platforms can provide the right fit to meet these modern data management needs.
Why cloud platforms & ETL on Cloud are inclined towards ELT?
Store data in bulk, but cost-effectively, to start with. Cloud platforms such as AWS S3, Snowflake, Azure Blog storage facilitates cost-effective storage options and can be ideal to build data lakes. Adapting the ELT approach enables quick data ingestion into the data lakes which can further be transformed into downstream data models for the identified analytics needs.
Another significance of the ELT approach is that it allows enterprises to continue to gain the benefits of a traditional DWH and BI, while future-proofing the analytics journey with Big data, Artificial Intelligence and Machine learning (AI &ML) powered analytics.
How do Cloud platforms & ETL on Cloud bring value?
By accommodating large volumes of data, enterprises can combine data with ML/AI techniques to acquire accurate predictions and prescriptions across business functions.
- Accommodate unstructured semi-structured data:
Enterprises may not know the value of data that they carry. Modern data approach guides enterprises to store all the data possibly in a Data lake without worrying about data’s value if not known at the time. Nevertheless, caution should be exercised to avoid using data lakes as dumping grounds, which can become unmanageable when data is dumped without giving a serious thought to it.
Cloud ETL supports various data sources, unstructured and semi-structured data, and there is prevalent use of streaming pipelines alongside the batch pipelines. With ELT approach, organizations can tap into data generated by IoT devices, sensors, log files etc. Different types of data can be extracted and loaded into the data lake wherein transformations are done leveraging the capabilities of cloud data warehouse.
- Achieve modern data management
Modern data management is more about ingesting huge volumes of data that can exist in any format and facilitating efficient processing to extract value from this data. ELT enables ingestion at scale, further augmented by leveraging the quick and efficient processing of columnar cloud Data warehouses like Azure Synapse, Snowflake and Redshift to name a few.
- Perform meaningful transformations
ELT also allows organizations to reap rewards from meaningful transformations. ELT allows exploration enabled by the loading of raw data and running queries for identifying suitable data transformations syncing with business requirements.
Saksoft’ s tryst with ETL on cloud
Saksoft has addressed clients’ modern data management needs by implementing ELT/ETL based cloud solutions. Very recently, one of our financial services customers was facing challenges with traditional data processes, legacy on-premise data architecture and big data management severely impairing their decision making capacity.
Data engineers at Saksoft created a winning combination out of Snowflake and cloud data warehouse to design an ELT based solution, extracting raw data from numerous sources and implementing cloud DWH to help the organization promote scalability, and achieve accelerated time to insights.
Another recent success story happened when our customer, a leading provider of digital offerings including online advertising and marketing services products, was facing time consuming data extraction, ingestion and transformation, as well as insight generation.
Saksoft stepped in to offer the ideal ETL on cloud solution. Matillion was identified and implemented as the cloud ETL tool to create automated data pipelines loading data from different sources into S3 data lake and then on to the Snowflake data warehouse. The leading provider of digital offerings benefited from real time visibility of data and faster delivery of data and insights.