Modern Data Stack - Creating value from the Data

Modern data stack is rapidly adopted by data teams, and innovation within it continues to accelerate. From the data warehouse to the data lake to the lakehouse, the rise of analytics engineering, real-time AI and intelligent decisioning

Modern Data Stack allows simplifying the data architecture for enterprises to handle all their data, analytics and AI and replicate data into a data warehouse, transform the data, and visualize insights for data-driven decision-making.

What Modern Data Stack Offers

• Lower barriers to use and easy to deploy
• designed for massive scale (in terms of data, users and Use cases)
• Composable data stack
• Version Control
• Automation

Why we adopt Modern Data Stack

• Greater efficiency
• Innovation through agility
• Velocity
• To Build incrementally and vertically
• Augmented BI

How Is the Modern Data Stack Different From a Traditional Data Stack?

What is a Modern Data Stack

The?Modern Data Stack?commonly refers to a collection of technologies that comprise a cloud-native data platform, generally leveraged to reduce the complexity in running a traditional data platform. The individual components are not fixed, but they typically include:?

• A Cloud Data Warehouse, such as?Snowflake,?Redshift,?BigQuery, or?Databricks Delta Lake
• A Data Integration Service, such as?Fivetran,?Segment, or?Airbyte
• A ELT data transformation tool, almost certainly?dbt
• A BI layer, such as?Looker?or?Mode?
• A Reverse ETL tool, such as?Census?or?Hightouch?

Future of Modern Data Stack

1. Rust and WASM will see early adoption - Modern Data Stack in a Box with DuckDB
2. Optimising the Data Cloud Cost
3. Business Intelligence will be moving towards RPA?(Workflows shift from centralized data teams to operational teams )
4. Data Activation: The Next Step After Analytics
5. Data Reliability - Data Observability and Quality will Converge
6. Metrics or Semantic Layer Adoption in early stage
7. Rise of Data Contracts
8. Rise of Active MetaData Platforms

Five key areas ripe for innovation in Modern Data Stack :?

• Artificial Intelligence?
• Data Sharing
• Data Governance
• Streaming
• Application Serving

How the enterprises Adopt Modern Data Stack

1. Building Centralised Data Platform - Step towards Data Mesh

• Data engineers focus on extract & load, with minimal transformations.
• The domain specific (data science) teams focus on the advanced transformations.
• Tooling should be made available to empower the teams.

2. Second Step Towards Scaling and Sharing Across teams

Scaling can be done on various dimensions, including getting more source data-sets, on-boarding more data science teams, or adding more empowering platforms as a service (think feature stores, model serving, and so on)

Key components of Modern Data Platform approach are:

• Agile (internal) customer focused approach.
• Platform thinking.
• Building Incrementally and Vertically.
• Removing bottlenecks, while providing a platform that is flexible and empowers the data science teams.
• Self sufficient teams, with freedom and autonomy. They are free to use the services that suits them and can autonomously prepare their data.

References