Apache Hadoop is a comprehensive ecosystem which now features many open source components that can fundamentally change an enterprise’s approach to storing, processing, and analyzing data.
Unlike traditional relational database management systems, Hadoop now enables different types of analytical workloads to run the same set of data and can also manage data volumes at a massive scale with advanced hardware and software applications. We can see many examples like CDH, which is Cloudera’s open source platform as popular distributions of Hadoop.
Distribution of Apache Hadoop
Here are some benefits of Hadoop distribution in database administration environments.
Hadoop offers a highly scalable architecture which is based on the HDFS file system that allows the organizations to store and utilize unlimited types and volume of data, all at an open source platform and industry-standard hardware.
With Hadoop, you can quickly integrate the existing applications or systems to move the data in and out of Hadoop through bulk loading processing with the help of Apache Sqoop. You can also live stream with the help of tools like Apache Kafka or Apache Flume, etc.
You can transform any complex data at varying scales using different Hadoop-compliant data access options like Apache Pig and Apache Hive for the batch MR2, or Apache Spark’s fastest in-memory processing. Process streaming of data as it enters into the cluster can be done through Spark Streaming.
The analysts can interact effectively with data on the go with the help of tools like Apache Impala, which acts as the Hadoop’s data warehouse. Using Impala, the analysts can experience business intelligence quality SQL performance and also optimum compatibility with all other BI tools. With the help of Cloudera Search and Apache Solr as specified at RemoteDBA.com, the analysts could accelerate their process of identifying inferable patterns in data in varying amounts and formats, in combination with Impala.
Using Hadoop technologies, the data analysts and data science can also be flexible in developing and iterating on advanced statistical models by effectively mixing up the partners technologies and open-source frameworks as Apache Spark.
Hadoop features a distributed data store, enabled through tools like Apache HBase, which can support fastest and random write/read which is mentioned to as fast data. This is inevitable in the case of online applications and e-commerce administration etc.
Differences between Apache Hadoop and RDBMS
Unlike Relational Database Management System (RDBMS), we cannot call Hadoop a database, but it is more of a distributed file system that can store and process a huge volume of data sets across a cluster of computers.
Hadoop has two major components: HDFS (Hadoop Distributed File System) and MapReduce. The former one is the storage layer of Hadoop which stores huge amounts of data. MapReduce is primarily a programming model which can effectively process the large data sets by converting them into different blocks of data. These blocks are distributed across the nodes on various machines in the cluster.
However, RDBMS is a structured database approach, in which data gets stored in tables in the forms of rows and columns. RDBMS uses SQL or Structured Query Language, which can help update and access the data present in different tables. As in the case of Hadoop, traditional RDBMS is not competent to be used in storage of a larger amount of data or simply big data.
Further, let’s go through some of the major real-time working differences between the Hadoop database architecture and the traditional relational database management practices.
In Terms of Data Volume
Volume means the quantity of data which could be comfortably stored and effectively processed. Relational databases surely work better when the load is low, probably gigabytes of data. This was the case for so long in information technology applications, but when the data size has grown to Terabytes or Petabytes, RDBMS isn’t competent to ensure the desired results.
On the other hand, considering Hadoop is the right approach when the need is to handle a bigger data size. Hadoop can be used to process a huge volume of data effectively compared to the traditional relational database management systems.
Considering the database architecture, as we have seen above Hadoop works on the components as:
- HDFS, which is the distributed file system of the Hadoop ecosystem.
- MapReduce, which is a programming model that help process huge data sets.
- Hadoop YARN, which helps in managing the computing resources in multiple clusters.
However, the traditional RDBMS will possess data based on the ACID properties, i.e., Atomicity, Consistency, Isolation, and Durability, which are used to maintain integrity and accuracy in data transactions. Such transactions would be of any sectors like banking systems, telecommunication, e-commerce, manufacturing, or education, etc.
It is the total data volume process over a specific time period so that the output could be optimized. Relational database management systems are found to be a failure in terms of achieving a higher throughput if the data volume is high, whereas Apache Hadoop Framework does an appreciable job in this regard. This is one major reason why there is an increasing usage of Hadoop in the modern-day data applications than RDBMS.
The diversity of data refers to various types of data processed. There are structures, unstructured, and semi-structured data available now. Hadoop possesses a significant ability to store and process data of all the above-mentioned types and prepare it for processing. When it comes to processing big volume unstructured data, Hadoop is now the best-known solution.
However, traditional relational databases could only be used to manage structured or semi-structured data, in a limited volume. RDBMS fails in managing unstructured data. However, it is very difficult to fit in data from various sources to any proper structure. So, we can see that Hadoop is the apt solution in handling data diversity than RDBMS.
The other major areas we can compare also include the response time wherein RDBMS is a bit faster in retrieving information from a structured dataset. But, even though Hadoop has a higher throughput, the latency of Hadoop is comparatively Laser. Hadoop has a significant advantage of scalability compared to RDBMS. Ultimately, when it comes to the matter of cost Hadoop is fully free and open source, whereas RDBMS is more of licensed software, for which you need to pay.
We hope we have provided the major differences between Hadoop and conventional RDBMS, which could help you to make the best choice for the purpose in hand.