SQL with Manoj

 
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch processing and stream processing methods, and minimizing the latency involved in querying big data.

It is a Generic, Scalable, and Fault-tolerant data processing architecture to address batch and speed latency scenarios with big data and map-reduce.

Lambda Architecture – Batch & Stream

–> The system consists of three layers: Batch Layer, Speed Layer & Service Layer

1. All data is pushed into both the Batch layer and Speed layer.

2. The Batch layer has a master dataset (immutable, append-only set of raw data) and pre-computes the batch views.

3. The Serving layer has Batch views for fast queries.

4. The Speed Layer compensates for processing time (to the serving layer) and deals with recent data only.

5. All queries can be answered by merging results from Batch views and Real-time views or pinging them individually.
 

Lambda Architecture with Azure:

Azure offers you a combination of following technologies to accelerate real-time big data analytics:

1. Azure Cosmos DB, a globally distributed and multi-model database service.

2. Apache Spark for Azure HDInsight, a processing framework that runs large-scale data analytics applications.

3. Azure Cosmos DB change feed, which streams new data to the batch layer for HDInsight to process.

4. The Spark to Azure Cosmos DB Connector

Azure Lambda Architecture simplified

How Azure simplifies the Lambda Architecture:

1. All data is pushed into Azure Cosmos DB for processing.

2. The Batch layer has a master dataset (immutable, append-only set of raw data) stored in Azure Cosmos DB. Using HDI Spark, you can pre-compute your aggregations to be stored in your computed Batch Views.

3. The Serving layer is an Azure Cosmos DB database with collections for the master dataset and computed Batch View for fast queries.

4. The Speed layer compensates for processing time (to the serving layer) and deals with recent data only. It utilizes HDI Spark to read the Azure Cosmos DB change feed. This enables you to persist your data as well as to query and process it concurrently.

5. All queries can be answered by merging results from batch views and real-time views, or pinging them individually.
 

–> For complete details check here in Microsoft Docs: Azure Cosmos DB: Implement a lambda architecture on the Azure platform