With the explosion of data being generated every day, managing and extracting insights from large datasets has become increasingly important. While proprietary solutions exist, many organizations are turning to open-source software which offers flexibility, scalability and cost savings. This article provides an overview of some of the most popular open-source platforms for distributed storage, processing and analytics of big data.
Big data comes in many forms, from logs, sensor data and user activity to financial transactions, scientific experiments and more. Effectively managing this scale of data requires specialized frameworks and infrastructure. Early open-source projects like Hadoop emerged to address this need, building clustered storage and parallel processing capabilities. Since then, the ecosystem has grown tremendously, with various projects each optimized for different types of big data workloads.
The Hadoop Ecosystem
The Apache Hadoop project kickstarted the modern big data revolution with its scalable distributed architecture. At its core are HDFS for storage and MapReduce for processing. HDFS provides redundancy and high throughput access to very large files across clusters. MapReduce executes user-defined map and reduce functions in parallel across large datasets. These basic primitives enabled new kinds of batch analysis that simply weren’t feasible before.
Over time, more capabilities were added to Hadoop. YARN was incorporated as a universal cluster resource manager, allowing various types of jobs beyond MapReduce. Projects like Hive, Pig and Spark SQL provided SQL-like interfaces on top of Hadoop. specialized frameworks emerged as well, such as HBase for NoSQL-style databases and Storm/Spark Streaming for real-time processing. The Hadoop ecosystem remains very active with new optimizations and functionality constantly added.
Apache Spark
While Hadoop shone for batch jobs, Apache Spark greatly accelerated interactive queries and streaming workloads with its in-memory computing model. Similar to MapReduce, Spark provides functional programming APIs and runs in distributed fashion. But it keeps working datasets in memory across the cluster for much faster iterative operations.
Spark also incorporated support for SQL (via Spark SQL), streaming, machine learning (via MLlib) and graph processing (via GraphX). This “Swiss army knife” design made it a good fit for a wider variety of big data use cases. Spark’s performance and ease of use helped drive its rapid adoption alongside Hadoop in enterprises. Further, it can run standalone or atop YARN to leverage existing Hadoop infrastructure.
Other Frameworks
Beyond Hadoop and Spark, other projects provide alternative open-source options for big data management depending on needs:
- Flink offers powerful streaming capabilities for stream processing, event-driven applications and machine learning on continuous data streams.
- Presto delivers fast SQL query performance atop diverse data sources like HDFS, object stores, NoSQL databases and more.
- Druid is tuned for interactive analytics on astronomical volumes of real-time event data like logs, metrics and IoT.
So in summary, the expanding open-source ecosystem provides the elastic scalability, cost savings and flexibility that modern big data demands, tackling problems from batch processing to interactive analytics to real-time decision making. Proper evaluation helps determine the right platform.
