Cazton has been a pioneer in Big Data Consulting and one popular technology that powers Big Data is Apache™ Hadoop. Hadoop is a highly scalable open-source framework written in Java, which allows processing and storage of terabytes or even petabytes of structured and unstructured complex data (Big Data) across clusters of computers. Its unique storage mechanism over distributed file system (HDFS) maps data wherever it is located on a cluster. The speciality of Hadoop is that it can scale from one server to hundreds of servers and can still perform well. It is fast, flexible and cost-effective as compared to traditional storage systems.
Hadoop's architecture is quite interesting. It is divided into four different modules called Hadoop Common, Hadoop YARN, Hadoop Distributed File System commonly known as HDFS and Hadoop MapReduce. Let us briefly take a look at what each of these modules are:
- Hadoop Common: This module is also known as Hadoop Core and is the collection of common utilities and libraries that support other Hadoop modules. This module also contains many JAR archives that are required to start Hadoop.
- Hadoop MapReduce: This module is the heart of Hadoop systems as it enables scalability. MapReduce is a combination of two different tasks (Map and Reduce). When a Hadoop system receives data, this module executes it in three different stages. The first stage is called Map stage, where data is converted into a combination of key-value pairs called Tuples. The second stage is called Shuffle stage, which is responsible for transferring data from Map stage to Reduce stage. Finally, the third called Reduce stage, which once it receives the tuples, processes them and creates a new set of tuples that are then sent to HDFS for storing.
- Hadoop Distributed File System (HDFS): This is the primary storage system used by Hadoop applications. It is based upon master/slave architecture where one master machine controls one or multiple slave devices and provides high performance access to large complex data across Hadoop clusters. It supports parallel processing as it divides the data into separate pieces and then quickly stores them on multiple nodes in a cluster.
- Hadoop YARN: This module, which was shipped in Hadoop v2.0, is basically a clustering platform that helps manage resources and schedule tasks. It allows multiple data processing engines, such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform thus unlocking an entirely new approach to analytics. The Apache software foundation describes Hadoop YARN as "next-generation MapReduce" or "MapReduce 2.0".
The Hadoop framework has a huge ecosystem with different kinds of technologies (both open source and commercial) that increases the capabilities of the four core modules we saw above. In this section, let's look at what some of those popular Hadoop technologies are.
- Spark: Spark is an open-source data analytics cluster computing framework that is fast and powerful for large-scale data processing. It runs programs up to 100x faster than Hadoop MapReduce in memory and 10x faster on disk. Want to know more about our Spark consulting service? Click here to continue reading.
- HIVE: HIVE data warehouse software enables easy querying, writing and managing large datasets that resides in distributed storage. It provides a SQL-like language called HiveQL for querying and also allows traditional map/reduce programmers to plug in their custom mappers and reducers in HiveQL. HIVE comes with a JDBC driver and a command-line tool for connection.
- Kafka: Kafka is an open-source stream processing platform that lets you publish and subscribe streams of data and stores them in a fault-tolerant way. It is horizontally scalable, fast and lets you collect big data, undertake real-time analysis and build streaming apps. Want to know more about our Kafka consulting service? Click here to continue reading.
- Impala: Impala is the highest performing SQL engine, which gives an RDBMS-like experience for querying large dataset from Hadoop Distributed File System. With Impala, users can communicate with HDFS or HBase using SQL queries in a faster way as compared to other SQL engines like Hive.
- PIG: Pig is an abstraction over MapReduce. It is a tool/platform that is used to analyze larger sets of data, representing them as data flows. It can be used to perform all data manipulation operations in Hadoop. It provides a high-level language known as Pig Latin, which can be used by developers to create their own functions to read, write and process data.
- HBase: HBase is an open-source, versioned, non-relational database modeled, distributed column-oriented database built on top of the Hadoop file system. It provides random real-time read/write access to data and leverages the fault-tolerance provided by the Hadoop Distributed File System (HDFS). We can store, read/access data into HDFS directly or via HBase.
- Zookeeper: Zookeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and group services to a large set of hosts. It solves this issue with its simple architecture and API.
- Ambari: A web-based tool for provisioning, managing, and monitoring Hadoop clusters, which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, Zookeeper, Oozie, Pig, and Sqoop.
- Cassandra: Cassandra database is the right choice when you need scalability and high availability without compromising performance. It support for replicating across multiple datacenters is best-in-class, providing lower latency for users and peace of mind of knowing that you can survive regional outages.
Other than the above-mentioned technologies, there are many more technologies like Solr, Flume, Mahout, Avro, Sqoop, Oozie that are also a part of Hadoop’s huge ecosystem. If you want to know more about these technologies or need consulting, contact us to learn more.
Why do we need Hadoop?
The following are the benefits of using Hadoop:
- Scalability: Hadoop enables scalability. Unlike traditional relational databases that do not scale horizontally, Hadoop comes as a huge replacement that allows big data to be stored across a cluster of one to hundreds of inexpensive servers. Though Hadoop requires a lot of servers, but the advantage is that it works well with inexpensive servers, making it a better and less expensive solution. Companies like Google and Facebook rely on Hadoop for managing and storing tons of datasets.
- Fast, Flexible and Foolproof: These 3F’s determine the real nature of Hadoop. Hadoop is extremely fast due to its distributed file and powerful mapping system. It processes data faster. It is flexible because Hadoop can generate value from your structured and unstructured data. It gives insights from various data sources like social media, emails and daily logs. And finally, it is foolproof, because when Hadoop stores data, it creates a replica of that data and stores it at multiple locations on different servers. So, if one node fails, the data still remains intact on the other one.
- Enables Advanced Data Analysis: When it comes to large datasets, people have started preferring Hadoop for managing, storing and processing the data. Hadoop makes it easy to work with large datasets and go for data analysis tasks in-house.
Comparing Hadoop with Relational Databases:
Many people get confused thinking of Hadoop as being some kind of a database system. But it isn't! Though both RDBMS and Hadoop support similar functions like storing, querying, managing, processing and manipulating data, both are widely different. RDBMS, which is a highly matured system, can only deal with structured data, whereas Hadoop, which is still maturing, has a distributed file system that enable storing and processing of structured, semi-structured and unstructured data. It is very difficult to scale-out RDBMS, which is not the case for Hadoop. RDBMS can be used where there's limited data, whereas Hadoop is one of the most popular framework chosen for Big Data. In RDBMS, database cluster uses the same data files stored in shared storage, whereas the storage data can be stored independently in each processing node. In some cases, performance tuning for RDBMS can be a bit problematic, whereas Hadoop enables hot tuning by adding extra nodes, which will be self-managed.
Comparing Hadoop with NoSQL Databases:
Initially, Hadoop and NoSQL databases appear to be similar, but there is still a big difference between the two. Hadoop, which is widely being known for its distributed file system, stores large datasets just like NoSQL databases would do; however, differences persist. NoSQL databases, which can process terabytes of data, falls short in comparison with Hadoop, which has the capability to process petabytes of data. That is way more than what NoSQL databases can handle!
NoSQL is a way to store data that does not require some sort of relation. This lends itself to processing that is similar to Hadoop. The use of a NoSQL database really depends on the type of problem that one is handling.
Hadoop is a system that is meant to store and process huge chunks of data. It is a distributed file system (DFS). The reason it does this is that central to its design is the assumption that hardware failures are common, thus making multiple copies of the same piece of information and spreading it across multiple machines and racks; so, if one goes down no problem, there are two more copies!
How can Cazton help you with Hadoop Consulting?
Due to an overwhelming demand in Big Data, Hadoop has become the most popular and favorite choice. We can provide you Big Data Architects, Big Data Specialists, Hadoop Consultants, Hadoop Architects, Hadoop Specialists, Hadoop Senior & Junior Developers per your requirements. Our speciality is that our team of experts range from developers having 2-3 years of experience to architects and specialists having 14-15 years of experience; thus we can provide a wide range of expertise and hourly rates, keeping your needs in mind.
Our team of experts is very strong in their Hadoop fundamentals and has knowledge about every layer of Hadoop stack. They have strong knowledge and expertise in understanding various modules of Hadoop Architecture, designing Hadoop clusters, performance tuning and setting up the top chain responsible for data processing. They are highly skilled in various Big Data tools like BigInsights, Cloudera, Hortonworks, MapR and have strong foundation in technologies like HDFS, HBase, Cassandra, Kafka, Spark, Storm, Scalr, Oozie, PIG, Hive, Avro, Zookeeper, Sqoop and Flume. Their strong analytical skills and problem-solving abilities, with great attention to details, is a boon for the clients.
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