ACTE offers a training course in Big Data and Hadoop. This course will equip you with a good level of knowledge in Big Data and Hadoop. Start building your own practical experience and learn useful skills for your personal growth with ACTE. The entire training course content is in line with these certifications programs and helps you clear these certification exams with ease. Start Learning with us ACTE Hadoop Classroom & Online Training Course.
Hadoop skills are in demand – this is an undeniable fact! Hence, there is an urgent need for IT professionals to keep themselves in trend with Hadoop and Big Data technologies. Apache Hadoop provides you with means to ramp up your career and gives you the following advantages: Accelerated career growth.
Hadoop is the supermodel of Big Data. If you are a Fresher there is a huge scope if you are skilled in Hadoop. The need for analytics professionals and Big Data architects is also increasing . Today many people are looking to pursue their big data career by grabbing big data jobs as freshers.
Even as a fresher, you can get a job in Hadoop domain. It is definitely not impossible for anyone to land a job in the Hadoop domain if they invest their mind in preparing and putting their best effort in learning and understanding the Hadoop concepts.
We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for Hadoop. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
A Hadoop Cluster uses Master-Slave architecture. It consist of a Single Master (NameNode) and a Cluster of Slaves (DataNodes) to store and process data. Hadoop is designed to run on a large number of machines that do not share any memory or disks. These DataNodes are configured as Cluster using Hadoop Configuration files. Hadoop uses a concept of replication to ensure that at least one copy of data is available in the cluster all the time. Because there are multiple copy of data, data stored on a server that goes offline or dies can be automatically replicated from a known good copy.
- To learn Hadoop and build an excellent career in Hadoop, having basic knowledge of Linux and knowing the basic programming principles of Java is a must. Thus, to incredibly excel in the entrenched technology of Apache Hadoop, it is recommended that you at least learn Java basics.
- Learning Hadoop is not an easy task but it becomes hassle-free if students know about the hurdles overpowering it. One of the most frequently asked questions by prospective Hadoopers is- “How much java is required for hadoop”? Hadoop is an open source software built on Java thus making it necessary for every Hadooper to be well-versed with at least java essentials for hadoop. Having knowledge of advanced Java concepts for hadoop is a plus but definitely not compulsory to learn hadoop. Your search for the question “How much Java is required for Hadoop?” ends here as this article explains elaborately on java essentials for Hadoop.
Apache Hadoop is an open source platform built on two technologies Linux operating system and Java programming language. Java is used for storing, analysing and processing large data sets. ... Hadoop is Java-based, so it typically requires professionals to learn Java for Hadoop.
Yes, you can learn Hadoop, without any basic programming knowledge . The only one thing matters is your dedication towards your work. If you really want to learn something, then you can easily learn. It also depends upon on which profile you want to start your work like there are various fields in Hadoop.
Our course ware is designed to give a hands-on approach to the students in Hadoop. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
Yes It is worth , Future will be bright. Learning Hadoop will definitely give you a basic understanding about working of other options as well. Moreover, several organizations are using Hadoop for their workload. So there are lot of opportunities for good developers in this domain. Indeed it is!
No Learning Hadoop is not very difficult. Hadoop is a framework of java. Java is not a compulsory prerequisite for learning hadoop. ... Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware.
Hadoop framework can be coded in any language, but still, Java is preferred. For Hadoop, the knowledge of Core Java is sufficient, and it will take approximately 5-9 months. Learning Linux operating system: - It is recommended to have a basic understanding and working of the Linux operating system.
Hadoop brings in better career opportunities in 2015.
Learn Hadoop to pace up with the exponentially growing Big Data Market.
Increased Number of Hadoop Jobs.
Learn Hadoop to pace up with the increased adoption of Hadoop by Big data companies.
Make a Career Change from Mainframe to Hadoop - Learn Why
Mainframe legacy systems might not be a part of technology conversations anymore but they are of critical importance to a business. The largest and critical industries across the globe - healthcare, insurance, finance and retail, still generate data from Mainframes. Mainframe data cannot be ignored because it drives mission critical applications across myriad industries. Can any distributed platform address mainframe workload? Is there an easy and cost effective way to make use of it? The answer is definitely a resounding YES.
Using Hadoop distributed processing framework to offload data from the legacy Mainframe systems, companies can optimize the cost involved in maintaining Mainframe CPUs. As increasing number of organizations are involved in Mainframe to Hadoop migration, to exploit big data – let’s take a look at what are the top skills, technicalities required and cost challenges involved, in migrating from Mainframe to Hadoop.
Need to Offload Data from Mainframes to Hadoop
- Organizations run critical applications on Mainframe systems, which generate huge volumes of data but lack the capability to support novel business requirements of processing unstructured data and also involve huge maintenance costs.
- The wealth of the data stored and processed in mainframes is vital but the resources required to manage data on mainframe systems are highly expensive. Businesses today, spend approximately $100,000 per TB, every year, to lock their data and back it up to tape. However, to manage the same amount of data on Hadoop –it costs $1000 to $4000.
- To address this huge cost of operation, organizations are increasingly offloading data to the Hadoop framework by shifting to clusters of commodity servers to analyse the bulk of their data. Offloading data to Hadoop might not be important but has potential benefits to the business, as the data is available to the analysts to explore and discern novel business opportunities, ensuring that no information is left untapped.
Challenges to be Successful with Hadoop and Mainframes
- Mainframe systems contain highly sensitive information whereas Hadoop manages data from diverse sources from harmless tweets to sensitive information. This implies that any data transfers from mainframes to Hadoop must be performed with utmost care, to ensure security. Organizations need to ensure that any software they install on mainframe systems to load data into Hadoop should be legitimate and have a good security track record.
- Many people think that moving mainframe data to Hadoop is very simple. However, this is not true, as there are several integration gaps, since Hadoop does not have any native support for mainframes.
- Mainframes and Hadoop both use different data formats - Hadoop uses ASCII format whereas Mainframes use packed decimal EBCDIC format.
- There is a huge skills gap, as both mainframe and Hadoop skills are in-demand. If finding a JCL or COBOL developer is difficult then finding a Hadoop developer who also understands mainframes is like trying to find a needle in haystack.
- Mainframe data has to be rationalized with COBOL copybook, which requires a special skillset that a person with only Hadoop skill might not possess and vice-versa. The switch from Mainframes to Hadoop is achievable and is a great technological adventure.
- There are many solutions from vendors like Syncsort, Veristorm, Compuware and BMC that target mainframe data with enhanced Hadoop ETL tools. Veristorm and Syncsort are developing various solutions to clear the bottleneck for organizations that still have valuable information locked in mainframe systems.
With the advent of scalable fault tolerant and cost effective big data technology like Hadoop, organizations can now easily reduce maintenance and processing expenses involved with mainframe legacy systems by including a Hadoop layer or by off-loading the batch processing data from Mainframes to Hadoop.
Hadoop fits well among COBOL and other legacy technologies, so, by migrating or offloading from mainframe to Hadoop, batch processing can be done at a lower cost, and in a fast and efficient manner. Moving from mainframe to Hadoop is a good move now, because of the reduced batch processing and infrastructure costs. Also, Hadoop code is flexible and easily maintainable, which helps in rapid development of new functionalities.
Organizations should begin with creating copies of selected mainframe datasets in HDFS and then migrating huge volumes of data from various semi-structured sources and RDBMs. The ultimate step is to migrate expensive batch mainframe workloads to Hadoop.
There are several Hadoop components that organizations can take direct advantage of, when offloading from Mainframes to Hadoop-
- HDFS, Hive and MapReduce components of the Hadoop framework help process huge legacy data, batch workloads and store the intermediate results of processing. Batch jobs can be taken off from mainframe systems, processed using Pig, Hive or MapReduce and the result can be moved back to mainframe systems which helps reduce MIPS (million instructions per second) cost.
- Sqoop and Flume components of the Hadoop framework helps move data between Hadoop and RDBMS.
- Oozie, component of the Hadoop framework, helps schedule batch jobs just like the job scheduler in mainframes.
Advantages of Using Hadoop with Mainframes for Legacy Workload
- Organizations can retain and analyse data at much granular level with longer history.
- Hadoop reduces the cost and strain on legacy platforms.
- Hadoop helps revolutionize enterprise workloads by reducing batch processing times for mainframes and EDW.
Why Mainframe Professionals should learn Hadoop ?
Huge Talent Crunch for “Mainframe + Hadoop” Professionals
- The lack of talent for “Mainframe+ Hadoop” skills, is becoming a persistent problem for the CIOs of any organization that want to push mainframe-hosted data and Hadoop-powered analysis closer together. Companies that still depend on mainframes are finding it difficult to hire professionals who possess mainframe knowledge along with Hadoop skills to support transaction processing, legacy applications and have the capability to leverage analytics from the data.
- The future is all set for Apache Hadoop and mainframes to rule the world of data managing systems. Organizations that are migrating from mainframes to Hadoop, are in search of professionals with knowledge of analytics. This is the best time for mainframe professionals to start updating their skillset with Hadoop.
