Big Data and Business Intelligence have gained momentum over the years and continue to do so. Enterprises are delving deeper into adapting Big Data analytics and also optimize the 3 V’s of Big Data which are Volume, Velocity and Variety. In order to gain more business insight and take better business decisions, it is imperative to balance between gaining volume as well as velocity under a single technical solution. What so ever domain it may be, custom software development, mobile app development, BI and Big Data, it is a difficult job to get relevant outputs under a single roof.
In the world of Big Data solutions, there prevail two different business objects or models – one is the traditional one and the other is the real-time processing. Big Data stalwarts like Apache Hadoop, MapReduce and Storm are known names following these models. But with advancement of technologies, following one of them isn’t sufficient and hence, what comes out as an ideal solution is a combination of both these model – i.e. an hybrid solution. With a concept of ‘getting the best of both worlds’, Nathan Marz created this concept of Lambda Architecture (LA) which gives out a scalable and comprehensive data processing architecture that gives fast results and efficient processing. The entire objective of Lambda Architecture is to build a fault tolerant system which can assess hardware failures and manual mistakes under a wide spread area of implementation.
As far as Apache Hadoop goes, there is availability of HDFS (reliable storage) and a processing system (MapReduce) amongst a whole lot of computers. Volume can be handled by Volume and Velocity can be dealt with real time tools that handle high latency systems with the incoming and outgoing data. LA takes care of merging both the batch and real time systems and make them execute in parallel.
How does Lambda Architecture execute?
Three distinct layers define the architecture for LA: Batch layer, Serving layer and Speed layer. Whatever data comes in for processing through the system goes to the batch layer as well as the speed layer. The batch layer takes care of managing the major master data set (immutable set of raw data) and also pre computing the batch views. The serving layer serves the job of indexing the batch view in order to fire easy queries with low latency. The speed layer handles the existing data and compensates for the high latency of serving layer. The batch and real time views, both in all, assist in finding a solution to any query coming in.
As far as Big Data technologies go, the Lambda Architecture implementation would be done on Apache Hadoop - Big Data with data being appended and batch processing. The other layers like speed and serving layers would be implemented on transactional databases like HBase, Cassandra and so on. A common staging layer with a middleware called Kafka can be used for the fresh data. And the evergreen Apache Storm could be used to process the data streaming in the speed layer.
Major Benefits of Lambda Architecture
At the time of Big Data management, the inclusion of a new layer to its architecture brings in a lot many advantages, such as:
- Accurate and perfect data processing with intact information like alerts, insights and so on.
- There is a fresh layer which is introduced and gains balance by reducing the random write storage requirements.
- Owing to the batch write storage, there is availability for data switching and versioning at certain intervals.
- A chance to overcome human mistakes owing to data sinks of raw data.
- Augmented data extraction to be put on the whole dataset.
- Imbibe immutability and re-computation onto the entire flow.
- An effective architecture which integrates the batch and stream processing and gives a reply to many use cases.
- Ad-hoc queries are executed as per varied types of data to get desired results.
How does Lambda Architecture fit for Big Data architecture?
Lambda Architecture can be applied to a diverse set of Big Data domains, one of which is Hadoop – Big Data framework that lets you store the data and add on fresh records to the master dataset. There need not be a exhaustive system to get individual records, now you just have to keep adding new records to the data set. If it is immutable records, the version of that particular instance is recorded. And newer versions keep getting created at a fresh entry. Hence, it becomes easy to handle bad records in this case.
Overall, Lambda Architecture has proven to be quite a rewarding and constructive model for amalgamating different Big Data analytics and attain multiple enterprise goals and objectives.