The Modern Data Stack Explained

What is Modern Data Stack?

Modern data stack (MDS)?is a collection of cloud-based tools and technologies designed to facilitate the efficient gathering, processing, storage, and analysis of data. This architecture addresses the increasing complexity and volume of data that organizations face today, enabling them to derive actionable insights and drive data-driven decision-making.

The modern data stack typically consists of several layers, each serving a specific function. Here are the key components that make the modern data stack (MDS):

Components of the Modern Data Stack

The modern data stack consists of technologies used to collect, store, manage, and analyze data in scalable ways. To understand this, lets delve into the six key components of the modern data stack, these includes.

• Data sources
• Data integration
• Data storage
• Data transformation
• Data analytics & visualization
• Data science & machine learning

1. Data Sources

In a modern data stack,?data sources?are the foundational elements that provide the raw data necessary for analysis and decision-making. These sources can be varied, encompassing both internal and external data. Here are some key examples of data sources commonly found in a modern data stack

• Web analytics tools – such as google analytics and Mixpanel.
• Customer relationship management (CRM) systems such as Salesforce and HubSpot.
• Databases e.g., PostgreSQL, MongoDB etc.
• Enterprise resource planning (ERP) systems such as SAP and Oracle ERP cloud.
• Event streaming platforms such as Apache Kafka and AWS kinesis.
• Social media platforms such as twitter API and Facebook Graph API.
• Ecommerce platforms such as Shopify and Stripe.
• IOT devices – (data from sensors and connected devices)

2. Data Integration

Data integration is a crucial component of the modern data stack, enabling organizations to consolidate data from various sources into a unified system for analysis and decision-making. Some of the tools that facilitate the movement of data from sources to storage solutions are.

• Fivetran
• Stitch
• Talend
• Airbyte etc.

3. Data Storage

Data storage?is a critical component that enables organizations to efficiently manage and analyze large volumes of data. This component typically includes two primary storage formats:?data warehouses?and?data lakes. Here’s an overview of these storage solutions and their roles in the modern data stack:

(i)?Data warehouse?– this is a centralized repository designed to store structured and semi-structured data. It is optimized for query performance and analytics.

Here data from various sources, such as transactional databases or CRM systems, is collected, cleaned, and transformed before being loaded into the warehouse. This allows for efficient querying and reporting on historical data.

Popular data warehouse solutions include. Snowflake, Amazon Redshift, and Google BigQuery.

(ii) Data lake?-?A data lake is a more flexible storage solution that can handle structured, semi-structured, and unstructured data. It allows for the storage of raw data in its native format until it is needed for analysis.

Data lakes support real-time data ingestion and are particularly useful for machine learning applications, as they can accommodate a wide variety of data types and formats.

Common data lake platforms include. AWS S3, Azure Data Lake Storage, and Google Cloud Storage.

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