There are several state-of-the-art tools available for building data pipelines. Here are some of the most popular ones: 1. Apache Airflow: Apache Airflow is an open-source platform for creating, scheduling, and monitoring workflows. It allows users to define complex workflows as code and provides a web-based interface for managing and monitoring them. 2. Apache Kafka: Apache Kafka is a distributed streaming platform that allows users to publish and subscribe to streams of records. It is commonly used for building real-time data pipelines and processing large volumes of data. 3. Apache NiFi: Apache NiFi is an open-source data integration platform that allows users to automate the flow of data between systems. It provides a web-based interface for designing and managing data flows and supports a wide range of data sources and destinations. 4. AWS Glue: AWS Glue is a fully managed ETL service that allows users to extract, transform, and load data from various sources into AWS data st...
"Data Lake" has become a buzzword over the past few years. In this age of big data where the volume of data is being increased day by day with rocket-like velocity and an enormous amount of varieties because of the increased use of streaming data, mobile, sensor data, etc., "Data Lake" has poised the headlines in the data community. So, it's important to know what "Data Lake" is. And here comes the human misconception where some of us mistakenly believe that "Data Lake" is just the Version 2.0 of the existing data storage system "Data Warehouse" and it's popular because of marketing hype. Though data warehouse or data lake both the entities store data but the data lake is fundamentally different from the data warehouse. And this article is about breaking that misconception and knowing the detail difference between the two. What is Data Warehouse? Is Data Warehouse Not Important Today? The traditional data warehouses ar...