In today's data-driven world, organizations are relying heavily on the use of data to make informed decisions. The modern data stack is a collection of technologies that allow businesses to store, process, and analyze large amounts of data efficiently. In this blog, we'll explore the modern data stack and the tools that are commonly used in each layer of the stack.
Data Sources Layer
The first layer of the modern data stack is the data sources layer. This layer consists of all the sources from which data is collected. This can include databases, cloud services, files, or any other data source. Some common tools used in this layer include:
MySQL: MySQL is a popular open-source relational database management system widely used for web applications.
Amazon S3: Amazon S3 is a cloud storage service provided by Amazon Web Services (AWS) widely used for storing and retrieving large amounts of data.
Google Cloud Storage: Google Cloud Storage is a cloud storage service provided by Google Cloud that allows users to store and access their data from anywhere.
Google Analytics: Google Analytics is a popular web analytics service provided by Google that allows users to track and analyze website traffic and user behaviour.
Appsflyer: Appsflyer is a mobile attribution and marketing analytics platform that allows users to track and analyze app installs, user engagement, and in-app events.
Stripe: Stripe is an online payment processing platform that allows businesses to accept payments securely and easily online.
Shopify: Shopify is an e-commerce platform that allows businesses to set up an online store and sell products online.
Data Integration Layer
The next layer of the modern data stack is the data integration layer. This layer involves combining data from different sources and transforming it into a format that can be easily analyzed. Some common tools used in this layer include:
Apache Kafka: Apache Kafka is an open-source distributed streaming platform that is widely used for real-time data processing and data integration.
Apache Airflow: Apache Airflow is an open-source platform that allows users to programmatically author, schedule, and monitor workflows.
Talend: Talend is a data integration tool that allows users to extract, transform, and load data from various sources into a single destination.
Stitch: Stitch is a cloud-based data integration platform that allows users to connect data from various sources, such as databases, SaaS tools, and APIs, and load it into a data warehouse for analysis.
Precog: Precog is a data integration platform that automates data integration, making it faster and more efficient for businesses to process and analyze data.
Data Warehousing Layer
The third layer of the modern data stack is the data warehousing layer. This layer involves storing data in a central repository for analysis. Some common tools used in this layer include:
Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS) widely used for storing and analyzing large amounts of data.
Google BigQuery: Google BigQuery is a cloud-based data warehousing service provided by Google Cloud that allows users to store and analyze large amounts of data.
Snowflake: Snowflake is a cloud-based data warehousing service that allows users to store and analyze data in a scalable and efficient way.
Data Processing Layer
The fourth layer of the modern data stack is the data processing layer. This layer involves processing and analyzing data stored in the data warehousing layer. Some common tools used in this layer include:
Apache Spark: Apache Spark is an open-source distributed computing system that is widely used for large-scale data processing and analysis.
Google Cloud Dataflow: Google Cloud Dataflow is a cloud-based data processing service provided by Google Cloud that allows users to process and analyze large amounts of data in real time.
Airflow: Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It enables users to define complex workflows as directed acyclic graphs (DAGs), making it easy to schedule, run, and monitor data processing pipelines.
Dbt: Dbt can be used to manage the entire data processing layer of the modern data stack, from loading data into a data warehouse to transforming and processing it for analysis. It is designed to integrate with popular data warehouses such as Snowflake, BigQuery, and Redshift, and provides features such as automated testing, version control, and documentation for data transformations.
Business Intelligence Layer
The final layer of the modern data stack is the business intelligence layer. This layer involves visualizing and presenting data in a way that can be easily understood by end-users. Some common tools used in this layer include:
Power BI: Power BI is a business intelligence tool provided by Microsoft that allows users to create and share interactive dashboards and reports.
Google Data Studio (Looker Studio): Google Data Studio is a free cloud-based BI tool that allows users to create and share interactive dashboards and reports. It integrates with a variety of data sources and provides a range of visualization options for displaying data.
Holistics: Holistics is a self-service BI platform that enables users to create and share interactive dashboards, reports, and SQL queries. It offers a range of visualization options and supports a variety of data sources, making it easy to integrate with existing data infrastructure.
Looker: Looker is a business intelligence tool that allows users to create and share interactive dashboards, reports, and data visualizations.
Tableau: Tableau is a business intelligence tool that allows users to create interactive dashboards and visualizations from their data.
QlikView: QlikView is a BI tool that provides advanced data visualization capabilities, allowing users to create interactive dashboards and reports with ease. It supports a variety of data sources and provides features such as data modelling, data blending, and advanced analytics.
In conclusion, the moder