Big Data and Hadoop training course in ACTE will help you learn about MapReduce, Hive, Pig, HDFS etc… Undergoing training in Hadoop and Big Data is quite advantageous to the individual in this data-driven world. Enhance your carrer opportunities by joining this training course in ACTE as more organizations work with Big Data. 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.
Get to Know About Hive Hadoop
How does the Hive make working so easy?
Hive is a data warehousing framework built on top of Hadoop which helps users for performing data analysis, querying on data and data summarization on large volumes of data sets. HiveQL is a unique feature that looks like SQL data stored in the database and performs the extensive analysis. Capable of reading data at a very high speed and writing the data into the data warehouses as well as it can manage large data sets distributed across multiple locations. Along with this hive also provides structure to the data that is stored in the database and users are able to connect to hive using command-line tool or JDBC driver.
What can you do with Hive?
- There are a lot of functionalities of the hive like data query, data summarization, and data analysis. Hive supports a query language called HiveQL or Hive Query Language. The Hive query language queries are translated into MapReduce job which is processed on the Hadoop cluster.
- Apart from this, Hive also reduces script that can be added into the queries. In this way, HiveQL increases the schema design flexibility which also supports data deserialization and data serialization.
Advantages
- The main advantage of Apache Hive is for data querying, summarization, and analysis. Hive is designed for better productivity of the developer and also comes with the cost of increasing latency and decreasing efficiency.
- Apache Hive provides for a wide range of user-defined functions that can be interlinked with other Hadoop packages like RHipe, Apache Mahout, etc. It helps developers to a great extent when working with complex analytical processing and multiple data formats. It is mainly used for data warehousingwhich means a system used for reporting and data analysis.
- It involves cleansing, transforming and modeling data to provide useful information about various business aspects which will help in producing a benefit to an organization. Data analysis a lot of different aspect and approaches which encompass diverse techniques with a variety of names in different business models, social science domains, etc.
- Hive is much user-friendly and allows users to simultaneously access the data increasing the response time. Compared to the other type of queries on huge data sets the hive’s response time is much faster than others. It is also much flexible in terms of performance when adding more data and by increasing the number of nodes in the cluster.
Why should we use the Hive?
Along with data analysis hive provides a wide range of options to store the data into HDFS. Hive supports different file systems like a flat file or text file, sequence file consisting of binary key-value pairs, RC files that stores column of a table in a columnar database. Nowadays the file that is most suitable with Hive is known as ORC files or Optimized Row Columnar files.
Why do we need Hive?
In today’s world Hadoop is associated with the most spread technologies that are used for big data processing. The very rich collection of tools and technologies that are used for data analysis and other big data processing.
Who is the right audience for learning Hive technologies?
Majorly people having a background as developers, Hadoop analytics, system administrators, data warehousing, SQL professional, and Hadoop administration can master of the hive.
How this technology will help you in career growth?
Hive is one of the hot skills in the market nowadays and it is one of the best tools for data analysis in the big data Hadoop world. Big enterprises doing analysis over large data sets are always looking for people with the rights of skills so can manage and query huge volumes of data. Hive is one of the best tool available in the market in big data technologies in recent days that can help an organization around the world for their data analysis.
Conclusion
Apart from the above-given functions hive has much more advanced capabilities. The power of hive to process a large number of datasets with great accuracy makes hive one best tool used for analytics in the big data platform. Besides, it also has great potential to emerge as one of the leading big data analytics tools in coming days due to periodic improvement and ease of use for the end user.
