TAKE ADVANTAGE OF OUR PROFESSIONAL ETL-AS-SERVICE & ETL-AS-CODE SOLUTION

Data is only as useful as it is fresh, accurate, and actionable. The analytics and business intelligence functions can only transform organization’s decision making when the data is delivered in a usable manner to the right hands in a timely manner thus helping teams to untap the potential of incredible amounts of data being produced.

At Polygon Data Labs, we help enterprises with Managed Cloud/In-Premise ETL and Data Streaming solutions. Pick the ETL /streaming tools of your choice depending on your use case and we can make the data integration and cloud adoption journey a breeze. Be it managed open-source tool like Apache AirFlow or Industry leader tools like Azure Synapse/Data Factory or traditional Ms SSIS.

Any Integration

Wherever your data might be stored, in whatever format and from whichever data source, we will access it. Skip the daunting task of data preparation to set up complex ETL processes: our ETL-as-a-service & ETL-as-code brings all the information your organization is using together, and you don’t need to do anything.

Migration and Cloud Adoption

We will help you orchestrate all of your SQL tasks elegantly with just a few lines of boilerplate code. We help migrate existing SQL ETL to the Cloud in a managed way. ETL-as-code tools allow to programmatically author, schedule and monitor your data pipelines using Python and SQL and housing it in a cloud service of your choice.

Managed Schema Changes

Whenever a change occurs in your data, our ETL as a service automatically handles it for you to ensure you never miss an important event. You don’t need to do anything: we can re-structure the underlying data schema for optimization purposes. That way, there are no limits to all the dashboard features we offer.

Mapping Made Easy

With our ETL as a Service, we convert your data of any type and from any source just the way you want it to be. Our Software figures out automatically the schema and enables automatic mapping of integrations. However, if you prefer to have full control over it, we can customize your mapping for you.

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Data Integration & Cloud Adoption for Flexible, Extensible and Scalable ETL

We help you build run, and manage data pipelines-as-code at enterprise scale with Apache Airflow, the most popular open source orchestrator.
We also help you solve the data processing & migration problems using Azure Data factory, an industry leading ETL tool . We will help you deploy the ETL-as- service in Azure with a blend of cloud and serverless options to save every cent of the dollars that you plan to spend.

Despite of many developments over past one decade, the problem of converting data and setting up complex ETL processes remained – without clean and harmonized information, no analysis is possible. This is why we offer this service along our BI software and relieve you from this pain point that requires time and resources.

WHAT ARE THE COMMON ETL PROCESS STEPS?

The ETL process includes the three steps that are forming its name: extraction, transformation and loading. The first step is extraction, which means connecting to a data source, and collecting the data needed. The objective of the extraction process is to retrieve this data with as little resources as possible, and it shouldn’t impact negatively the data source in terms of performance, response time or any type of locking.

Secondly, the transformation step executes a set of rules or functions to convert the extracted data to the standard format. It is thus prepared and ‘cleaned’, to be loaded into the end target. This process may be near to real-time, to a couple of hours to several days, depending on the size and the quality of the data source, but also on the business and technical requirements of the target data warehouse or database.

Finally, the loading step will import the extracted and cleaned data into the target database or warehouse. Depending on the requirements, the information can go through an overwriting process to include cumulative information; otherwise, the new data can be added at regular intervals, in an historic form. How often and how much is added or replaced vary according to the resources available, but also depending on the business needs.

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