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Modern data stack

Businesses have already realized that many data processing tools are outdated and cannot effectively solve up-to-date data tasks. Now there is a process of moving data from outdated database systems. Systems of new solutions replace outdated systems. The set of new solutions is referred to as the "modern data stack" (MDS).

What does Modern Data Stack Mean?

Data is one of the most valuable assets of any enterprise. Moreover, its value will only increase. However, fragments of data themselves do not have much worth. Data must be collected, organized, cleaned, and analyzed to generate value. So, a data stack is a set of technologies that developers need to pass data through all these steps. It is a suite based on which an IT product is developed: programming languages, frameworks, database management systems, compilers and so on. A new approach to data integration allows data engineers to save time and effort. MDS creates clean, reliable, and always available data.

What Does Modern Data Stack consist of?

Modern data stack for data integration consists of several layers laid on top of each other. Each layer has its purpose:

Data warehouse (Redshift, Snowflake, BigQuery)

Data ingestion and integration (Fivetran, Event Hub, Airbyte, Stitch, Segments)

Data transformation (dbt, Airflow,EasyMorph)

Business intelligence (Looker, Tableau, Mode)

Data governance (Atlant, Immuta, Informatica)

What sets are used to build a modern stack in data projects?

When creating a data stack, data engineers should pay attention to each stage. It has to be filled with tools that meet the goals and needs of the business. In addition, integration tools can significantly simplify the work process.

The most important step is to build the data warehouse. The data stack will largely depend on the data storage a company chooses. Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse are among the most popular tools available today.

The first component of the data stack is the ingestion tool. The task of this step is to filter the data before it enters the data store.

The next step is to choose reliable tools for data transformation. This stage needs SQL knowledge.

The business intelligence stage also needs reliable and high-performance tools as it maximizes data value. These tools have to match business goals. So it is essential to define a company's goals first and then choose the data data visualization tool.

The last step of the modern data stack is the reserve-ETL solution. This framework is the new advanced data integration platform and pipeline technology. Reverse ETL has become a central part of the modern data stack to close the loop between analysis and action or activation.

DataEngi Modern data stack includes Fivetran for ELT, Amazon Redshift for storage, Tableau for BI. It is quickly configured, has a small cost, and doesn't require much maintenance.

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Stack is important

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