As we know organizations from different domains are investing in big data analytics nowadays by analyzing large data sets to uncover all the hidden patterns unknown correlations, market trends, customer preferences and other useful business information. These analytical findings are helping organizations in more effective marketing, new revenue opportunities and better customer service and they're trying to get competitive advantages over rival organizations and other business benefits. An Apache Spark and Hadoop are the two of most prominent big data frameworks and people often comparing these two technologies.
What is Hadoop?
Hadoop is a framework to store and process large sets of data across computer clusters and Hadoop can scale from single computer systems up to thousands of commodity systems that offer a local storage and compute power. Hadoop is composed of modules that work together to create the entire Hadoop framework these are some of the components that we have in the entire Hadoop framework or in its ecosystem. For example HDFS, which is the storage unit of Hadoop yarn which is for resource management there are different analytical tools like Apache hive, Pig, NOSQL databases like Apache H-Base even Apache Spark and Apache stone in the Hadoop Ecosystem for processing big data in real-time.
For ingesting data we have tools like flume and scooped flume is used to ingest structured data whereas coop is used to ingest structured data into HDFS.
What is Spark?
A Spark is a lightning-fast cluster computing technology that is designed for fast computation. The main feature of Spark is its in-memory cluster computing that increases the processing of speed of an application for perform similar operations to that of Hadoop modules but it uses an in-memory processing and optimizing the steps.
SPARK V/S HADOOP
Spark performs better than Hadoop when:
Data size ranges from GBs to PBs.
There is a varying algorithmic complexity, from ETL to SQL to machine learning.
Low-latency streaming jobs to long batch jobs.
Processing data regardless of storage medium is it disks, SSDs, or memory.
We can get clear understanding of Hadoop and Spark by following observations:
Performance: Spark is fast because it has in-memory processing it can also use disk for data that doesn't fit into memory. Sparks in memory processing delivers near real-time analytics and that makes spark suitable for credit cards. While in Hadoop data is move through disk and network.
Ease of Use: Spark comes with user-friendly API's for Scala, Java, Python, and Spark SQL etc. While in Hadoop we can ingest data easily by using or integrating it with multiple tools like Sqoop, Flume, Pig and Hive.
Cost: Hadoop and Spark both are open source projects so there is no cost for the software cost is associated with the infrastructure both the products are designed in such a way that it can run on commodity hardware with low TCO total cost of ownership.
Similarities between Spark and Hadoop:
Let us look at how using both together can be better than siding with any one technology.
Hadoop components can be used alongside Spark in the following ways:
1. HDFS: Spark can run on the top of HDFS to leverage the distributed replicated storage.
2. MapReduce: Spark can be used along with MapReduce in the same Hadoop cluster or separately as a processing framework.
3. YARN: Spark applications can be made to run on YARN (Hadoop NextGen).
4. Batch and Real Time Processing: MapReduce and Spark are used together where MapReduce is used for batch processing and Spark for real-time processing.