Hadoop Framework works on the following two core components :
HDFS – Hadoop Distributed File System is the java-based file system for scalable and reliable storage of large datasets. Data in HDFS is stored in the form of blocks and it operates on the Master Slave Architecture.
Hadoop MapReduce-This is a java-based programming paradigm of Hadoop framework that provides scalability across various Hadoop clusters. MapReduce distributes the workload into various tasks that can run in parallel. Hadoop jobs perform 2 separate tasks- job. The map job breaks down the data sets into key-value pairs or tuples. The reduce job then takes the output of the map job and combines the data tuples to into smaller set of tuples. The reduce job is always performed after the map job is executed.
Hadoop has its strengths and difficulties. Business needs more specialized skills and data integration to factor into planning and implementation. Even though this happens, a large percentage of Hadoop implementations fail.
To help others avoid common mistakes with Hadoop, explore this article and know top 5 mistakes with Hadoop and how to avoid them.
MISTAKE 1: MIGRATE EVERYTHING BEFORE DEVISING A PLAN
As attractive as it can be to dive, head first into Hadoop, never start without a plan. Migrating everything without a clear strategy will only create long-term issues resulting in expensive ongoing maintenance. With first-time Hadoop implementations, users can expect a lot of error messages and a steep learning curves.
Successful implementation starts by identifying a business use case. Consider every phase of the process – from data ingestion to data transformation to analytics consumption, and even beyond to other applications and systems where analytics must be embedded. It also clearly determines how Hadoop and big data will create value for the business.
Our advice: Maximize your learning in the least amount of time by taking a holistic approach and starting with smaller test cases.
MISTAKE 2: ASSUME RATIONAL DATABASE SKILLSETS ARE TRANSFERABLE TO HADOOP
Hadoop is a distributed file system, not a traditional relational database (RDBMS). User can’t migrate all their relational data and manage it in Hadoop, nor can expect skillsets to be easily transferable between the two.
If the team is lacking Hadoop skills, it doesn’t necessarily mean you have to hire all new people. Every situation is different, and there are several options to consider. It might work best to train existing developers. User might be able to plug skills gaps with point solutions in some instances, but growing organizations tend to do better in the long run with an end-to-end data platform that serves a broad spectrum of users.
Our advice: It is important to look for software, along with the right combination of people, agility, and functionality to be successful. There are lot of tools available which automates some of the repetitive aspects of data ingestion and preparation.
MISTAKE 3: USER’S FIGURE-OUT SECURITY LATER
High profile data breaches have motivated most enterprise IT teams to prioritize protecting sensitive data. If the user considers using of big data, it’s important to keep in mind while processing sensitive data about the customers and partners. The user should never, ever, expose the card and bank details, and personally identifiable information about the clients, customers or the employees. Protection starts with planning ahead.
Our advice: Address each of the security solutions before deploying a big data project. Once a business need for big data has been established, decide who will be benefited from the investment and how it is going to impact the infrastructure.
MISTAKE 4: BRIDGING THE SKILLS GAP WITH TRADITIONAL ETL
Plugging the skills gap can be tricky for the organizations who are considering to solve big data’s ETL challenges. Many developers are proficient in Java, Python, and HiveQL, but may lack the experience to optimize performance on relational databases. When Hadoop and MapReduce are used for large scale traditional data management workloads such as ETL, this problem will be increased.
Some point solutions can help to plug the skills gap, but these tend to work best for experienced developers. If you’re dealing with smaller data sets, it might work to hire people who’ve had the proper training on big data and traditional implementations, or work with experts to train and guide staff through projects. But if you’re dealing with hundreds of terabytes of data, then you will need an enterprise-class ETL tool as part of a comprehensive business analytics platform.
Our advice: People, experience, and best practices are essential for successful Hadoop projects. While considering an expert or a team of experts as permanent hires or consultants, user should consider their experience with “traditional” as-well-as big data integration, the size and the complexity of the projects they’ve worked on, the organizations they worked with, and the number of successful implementations they have done. While dealing with large volumes of data, it might be the time to evaluate a comprehensive business analytics platform which is designed to operationalize and simplify Hadoop implementations.
MISTAKE 5: ENTERPRISE-LEVEL VALUE ON A SMALL BUDGET
The low-cost scalability of Hadoop is one of the reasons why organizations decide to use it. But many organizations fail to factor in data replication/compression, skilled resources, and overall management of big data integration of the existing ecosystem.
Hadoop is built to process enormous data files that continue to grow. It’s essential to do proper sizing up front. This includes having the skills on hand to leverage SQL and BI against data in Hadoop and to compress data at the most granular levels. The compression of data also needs to be balanced with performance expectations for reading and writing data. Also, storing the data may cost 3x more than what the user has planned initially.
Our advice: Understand how the storage, resources, growth rates, and management of big data will factor into your existing ecosystem before you implement.
As we know, Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, excessive processing power and the ability to handle virtually boundless concurrent tasks. Hadoop is powerful, but, like most of the systems, it has some sharp edges. Explore this article and know what are the pain points of Hadoop
Hadoop isn’t a database
Hadoop is amply different from an access and storage perspective to throw a lot of people off. Databases abstract away the details of on-disk organization, file formats, and serialization, partitioning, optimization for varied access patterns. Topics such as “data modeling” are treated either at the logical layer or assumed as a relational engine. As an example, most of the people are not aware of how relational database engines performs various forms of joins.
Hadoop is a distributed system
Deployment, composition, management, monitoring, and debugging a single threaded, single process system can be tough. A multithreaded single and multi-process system is harder. A multi-threaded, multi-process, and distributed system is harder. Hadoop has a ton of moving parts and while it gets better with each release, it’s still a complex system that requires specialized knowledge. That said, this isn’t dissimilar from other systems. The main stumbling block is that most people don’t have tons of experience with distributed systems.
Hadoop has a huge ecosystem
There are a huge number of open sources and commercial products/projects have hop-up around Hadoop that interoperate with it in some way. Each of these comes with its own complications. More than a single system, Hadoop is an entire world until itself.
Hadoop is evolving
In the grand scheme of things, Hadoop is a young system. It’s evolving and changing at an extremely rapid pace. Hence, there are a huge number of things to keep up with if you want to know all the details.
Hadoop tooling is still developing:
Many existing tools and similar systems are designed to deal with the data that resides in relational databases. While the ecosystem is growing at a tremendous rate, not all of the tools you might expect have been fully updated in support of HDFS and Hadoop MapReduce. But, many of the commercial vendors in the ETL, EDW, BI and analytics spaces are well on their way. Some have already arrived.
Hadoop is still a young technology– it’s clear that lots of organizations need more resources, competence, solutions, and tools to relieve the execution difficulties. Each week we see brand-new market entrants, which are accelerating the rate of Hadoop adoption. In fact, different verticals are including their own unique set of devices that satisfy demands such as integrated security and regulatory compliance capabilities. Hadoop-experimentation is drawing to a close, now the developers are going into a phase of fast adoption and even a little beyond the early adopter phase, because companies are producing finest practices, looking for standardization and ease-of-use so that users can successfully obtain understandings at a faster speed.
What’s the difference between Hadoop 1.x and Hadoop 2.x?
HDFS federation brings important measures of scalability and reliability to Hadoop. YARN, the other major advance in Hadoop 2, brings significant performance improvements for some applications, supports additional processing models, and implements a more flexible execution engine.
YARN is a resource manager that was created by separating the processing engine and resource management capabilities of MapReduce as it was implemented in Hadoop 1. YARN is often called the operating system of Hadoop because it is responsible for managing and monitoring workloads, maintaining a multi-tenant environment, implementing security controls, and managing high availability features of Hadoop.
Like an operating system on a server, YARN is designed to allow multiple, diverse user applications to run on a multi-tenant platform. In Hadoop 1, users had the option of writing MapReduce programs in Java, in Python, Ruby or other scripting languages using streaming, or using Pig, a data transformation language. Regardless of which method was used, all fundamentally relied on the MapReduce processing model to run.