What makes hadoop different

Hadoop Provide Flexibility:. Skip to content. Change Language. Related Articles. Table of Contents. Save Article. Improve Article. Like Article. Last Updated : 25 Aug, Next Amazon Interview Experience 2 months Internship. Recommended Articles. Article Contributed By :. Easy Normal Medium Hard Expert. Writing code in comment? Please use ide.

Load Comments. Although Pig and Hive have similar functions, one can be more effective than the other in different scenarios. Pig is useful in the data preparation stage, as it can perform complex joins and queries easily.

It also works well with different data formats, including semi-structured and unstructured. Hive , however, works well with structured data and is therefore more effective during data warehousing. It's used in the server side of the cluster. Researchers and programmers tend to use Pig on the client side of a cluster, whereas business intelligence users such as data analysts find Hive as the right fit. Flume is a big data ingestion tool that acts as a courier service between multiple data sources and the HDFS.

It collects, aggregates, and sends huge amounts of streaming data e. Data sources communicate with Flume agents — every agent has a source, channel, and a sink. The source collects data from the sender, the channel temporarily stores the data, and finally, the sink transfers data to the destination, which is a Hadoop server. While Flume works on unstructured or semi-structured data, Sqoop is used to export data from and import data into relational databases.

As most enterprise data is stored in relational databases, Sqoop is used to import that data into Hadoop for analysts to examine. Database admins and developers can use a simple command line interface to export and import data. Sqoop is also fault-tolerant and performs concurrent operations like Flume. Zookeeper is a service that coordinates distributed applications. In the Hadoop framework, it acts as an admin tool with a centralized registry that has information about the cluster of distributed servers it manages.

Some of its key functions are:. All write-operations from clients need to be routed through the leader, whereas read operations can go directly to any server. Zookeeper provides high reliability and resilience through fail-safe synchronization, atomicity, and serialization of messages. Kafka is a distributed publish-subscribe messaging system that is often used with Hadoop for faster data transfers. A Kafka cluster consists of a group of servers that act as an intermediary between producers and consumers.

In the context of big data, an example of a producer could be a sensor gathering temperature data to relay back to the server. Consumers are the Hadoop servers. The producers publish message on a topic and the consumers pull messages by listening to the topic. A single topic can be split further into partitions. All messages with the same key arrive to a specific partition. A consumer can listen to one or more partitions. By grouping messages under one key and getting a consumer to cater to specific partitions, many consumers can listen on the same topic at the same time.

Thus, a topic is parallelized, increasing the throughput of the system. Kafka is widely adopted for its speed, scalability, and robust replication. One of the challenges with HDFS is that it can only do batch processing.

So for simple interactive queries, data still has to be processed in batches, leading to high latency. HBase solves this challenge by allowing queries for single rows across huge tables with low latency. It achieves this by internally using hash tables.

HBase is scalable, has failure support when a node goes down, and is good with unstructured as well as semi-structured data. Hence, it is ideal for querying big data stores for analytical purposes. Though Hadoop has widely been seen as a key enabler of big data, there are still some challenges to consider.

These challenges stem from the nature of its complex ecosystem and the need for advanced technical knowledge to perform Hadoop functions. However, with the right integration platform and tools, the complexity is reduced significantly and hence, makes working with it easier as well. To query the Hadoop file system, programmers have to write MapReduce functions in Java. For this reason, if a user has a use-case of batch processing, Hadoop has been found to be the more efficient system.

Both Spark and Hadoop are available for free as open-source Apache projects, meaning you could potentially run it with zero installation costs. However, it is important to consider the total cost of ownership, which includes maintenance, hardware and software purchases, and hiring a team that understands cluster administration.

The general rule of thumb for on-prem installations is that Hadoop requires more memory on disk and Spark requires more RAM, meaning that setting up Spark clusters can be more expensive. Additionally, since Spark is the newer system, experts in it are rarer and more costly. Extract pricing comparisons can be complicated to split out since Hadoop and Spark are run in tandem, even on EMR instances, which are configured to run with Spark installed.

For a very high-level point of comparison, assuming that you choose a compute-optimized EMR cluster for Hadoop the cost for the smallest instance, c4. Therefore, on a per-hour basis, Spark is more expensive, but optimizing for compute time, similar tasks should take less time on a Spark cluster.

Hadoop is highly fault-tolerant because it was designed to replicate data across many nodes. Each file is split into blocks and replicated numerous times across many machines, ensuring that if a single machine goes down, the file can be rebuilt from other blocks elsewhere.

Data across Spark partitions can also be rebuilt across data nodes based on the DAG. Data is replicated across executor nodes, and generally can be corrupted if the node or communication between executors and drivers fails.

Apache Sentry, a system for enforcing fine-grained metadata access, is another project available specifically for HDFS-level security. Hadoop uses Mahout for processing data. Mahout includes clustering, classification, and batch-based collaborative filtering, all of which run on top of MapReduce. This is being phased out in favor of Samsara, a Scala-backed DSL language that allows for in-memory and algebraic operations, and allows users to write their own algorithms.

Spark has a machine learning library, MLLib, in use for iterative machine learning applications in-memory. There are several instances where you would want to use the two tools together. Below you can see a simplified version of Spark-and-Hadoop architecture:. Organizations that need batch analysis and stream analysis for different services can see the benefit of using both tools.

Hadoop can—at a lower price—deal with heavier operations while Spark processes the more numerous smaller jobs that need instantaneous turnaround. Thus, Hadoop and YARN in particular becomes a critical thread for tying together the real-time processing, machine learning and reiterated graph processing.

So is it Hadoop or Spark? These systems are two of the most prominent distributed systems for processing data on the market today. Hadoop is used mainly for disk-heavy operations with the MapReduce paradigm, and Spark is a more flexible, but more costly in-memory processing architecture.

For more information on alternatives, read our Hive vs Spark comparison. Platform Overview. Features Alerts. Fully-Managed ELK.