Hadoop is a de facto standard for storing and analyzing large amounts of data as batches, but this can be incomplete due to continuous data generation. Lambda Architecture proposes combining Hadoop's batch processing with stream processing to analyze all generated data in real-time, utilizing messaging systems, distributed stream processing systems like Storm or Spark Streaming, and databases that meet high throughput and low latency requirements. An application queries processed data from both Hadoop and the database to create a complete view of results, raising questions about storing processed data in either Hadoop or the database, with options including exporting from Hadoop to Couchbase Server or storing it directly in the database. The combination of batch and stream processing enables real-time analysis of all generated data, allowing for more granular insights and better predictive analytics.