The traditional data warehouse model is no longer suitable for handling the rapid growth of internet and mobile data, which has led to a shift in focus towards big data. Hadoop emerged as a solution to capture and process large amounts of data at scale, but its strengths lie in storing and processing large volumes of data rather than providing fast results. The traditional approach of using MapReduce for batch processing is often replaced by faster processing engines like Spark, while the Hadoop Distributed File System (HDFS) provides cheap storage but lacks mechanisms for fast ingest or access. To tackle fast data requirements, companies need solutions that can handle fast data ingestion, low latency queries, and high concurrency. A new architecture has emerged to address these needs, which includes a real-time data warehouse, a data lake, and application/message queue components. This approach enables businesses to have instant access to data, allowing them to respond quickly to changing conditions and stay competitive in the market.