I’ve been reading a lot about data engineering and one thing I want to share is what I learned about modern data stack. It was mainly define as a collection of tools and technologies that organizations use to gather, process, and analyze data.
It helps organizations quickly and easily derive insights from their data and make data-driven decisions that drive business value.
🙂It's more like a “lego” (Like a toolbox from which to pick the tools you need).
Common components of modern data stack include :
1️⃣Data storage
Storage typically stand for a database or data lake where raw data is stored. Modern data stacks often use distributed systems like Hadoop, Warehouses like BigQuery to store large amounts of data.
2️⃣Data processing
This is where raw data is transformed into a format that is more suitable for analysis. Common data processing tools include Apache Spark, Apache Flink and Google Cloud Data Fusion.
3️⃣Data visualization
Data visualization tools such as Tableau, PowerBI, QlikView and Google Data Studio are often used to present processed data in a visual format, enabling organizations to gain insights and understand trends more easily.
4️⃣Machine learning
Many modern data stacks include tools for building and deploying machine learning models easily. These can be used to make predictions or take automated actions based on the data. Some popular tools for machine learning include TensorFlow, scikit-learn and Google Cloud AI Platform…
5️⃣Data governance
As organizations accumulate and manipulate more data, it is essential that they ensure the data is accurate, secure, and follows regulations. Tools like Collibra and Alation can assist with data governance in a modern data stack to achieve these goals.
A modern data stack is designed to help organizations extract insights from their data efficiently and effectively, allowing them to make data-driven decisions that contribute to the success of the business 🚀.
🤔 What are your thoughts about modern data stack ?