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Big Data Hadoop And Spark Developer Training

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Job Oriented Tools Covered in Big Data Hadoop And Spark Developer Training Certification

Get Train Our Effective Big Data Hadoop And Spark Developer Course

  • Big Data Hadoop in combination with the Spark Training course is intended to give you a detailed insight into the distributed framework invited to take on the challenges of big data.
  • The training on Hadoop along with its Ecosystem tools and Spark's extremely fast programming framework are explained, including fundamental elements for Linux OS, the industry's Server OS.
  • You will also learn how to handle and analyze large database sets stored in HDFS with Pig, Hive, Python, and Spark and how to use Sqoop to ingest from and into RDBMS, Big Data Database – the No-SQL database.
  • The best Spark training facility can help you master the real-time processing of data with Spark.
  • Implementation of applications by Spark, comprehension of parallel processing by Spark using Big Data RDDs, and Sparks capabilities.
  • The Big Data sector has an immense shortage of professionals who take the time to take advantage of it.
  • At ACTE Training we help students get the certificates they need. Moreover, we provide a completion certificate for each course that we offer.
  • START YOUR CAREER WITH Big Data Hadoop And Spark Developer CERTIFICATION COURSE THAT GETS YOU A JOB OF UPTO 8 TO 17 LACS IN JUST 80 DAYS!
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
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Course Objectives

While industry domain expertise is vital, becoming a certified Big Data Hadoop And Spark Developer specialist means you have a wide range of employment options across sectors and are not limited to one. This is critical for keeping relevant and adaptable in a competitive talent market. There are numerous benefits to being a Big Data Hadoop And Spark Developer specialist, including professional development, greater compensation, additional job alternatives, and the chance to update to new technologies. You will also work for several major businesses.

Here are a few reasons why you should take Big Data Hadoop And Spark Developer classes.

  • The Big Data Hadoop And Spark Developer industry is growing at a compound annual growth rate of 33.5 percent (CAGR).
  • After 10 years, the Big Data Analytics Industry will be worth $20 billion, with Big Data and AI technologies accounting for about 30% of the net income.
  • To gather, clean, organize, process, and analyze data from many sources in order to extract valuable insights and information.
  • Discovering new data sources and developing techniques for improved data mining, analysis, and reporting.
  • SQL queries will be required to extract data from the data warehouse.

Here are the top 10 trends in retail banking :

  • A qualified Big Data Hadoop And Spark developer's average yearly compensation is around $98,000. The wage might vary depending on the experience.
  • Owing to its rich features and capabilities, many leading firms have moved to Golang.
  • Big Data Hadoop And Spark development experts have enough employment options right now, and their popularity increases every day.
  • The specialists of Big Data Hadoop and Spark Developers get high pay in comparison to other IT professionals.

After finishing the Big Data Hadoop And Spark Developer certification course in collaboration with IBM, you will have the skills necessary to help you achieve your desired job, which includes

  • Data Scientist/Big Data Scientist.
  • Technical Manager.
  • Program Manager.
  • Big Data/Hadoop Developer.
  • Product Engineer.
  • Big Data Lead Data Architecture.

A job in data analytics is not only a feasible option but also one of the most popular these days. A Master's degree in Data Analytics can help you find a job in a range of sectors and organizations all around the world.

  • R programming.
  • Tableau Public.
  • QlikView.
  • RapidMiner.
  • KNIME.
  • Excel.
  • Apache Spark.
  • Splunk.
  • SAS.

What are the criteria for pursuing a career in Big Data Hadoop And Spark Developer?

Big data refers to massive quantities of data. It is not to be confused with big data analytics. Big data analytics is concerned with getting insights from past events and, more importantly, about future occurrences via the use of predictive models. Probability and statistics fundamentals. A working understanding of a programming language such as Python. Understand the fundamentals of an operating system. Patience and desire to learn are required. While a typical data analyst may be able to function without becoming a full-fledged programmer, a big data analyst must be quite acquainted with coding.

Is it possible to study Big Data Hadoop And Spark Developer without prior programming experience?

Because analysts and researchers have existed long before big data, data analyst responsibilities are clearly defined. Data analysts do not need sophisticated coding abilities, but they should be familiar with analytics tools, data visualization software, and data management software. The response is emphatical “ No ” Today, there are several open-source tools available that do not require programming skills and can be readily utilized by big data analysts to analyze and examine data.

Will I Receive Adequate Practical Training in Big Data Hadoop And Spark Developer?

Our courseware is designed to provide students with a hands-on approach to Big Data Hadoop And Spark Developer. The course consists of theoretical courses that teach the fundamentals of each module, followed by high-intensity practical sessions that represent the industry's current issues and demands, which will necessitate the students' time and commitment.

Is it worthwhile to learn Big Data Hadoop And Spark Developer?

Yes, it is possible to learn Big Data Hadoop And Spark Developer. Data Science is the creation of methods for recording, storing, and analyzing data in order to derive usable information. Big Data Hadoop And Spark Developer's objective is to acquire information and understanding from any sort of data, structured or unstructured. It is worthwhile to understand big data.

Is it tough to understand Big Data Hadoop And Spark Developer?

No, It is not difficult to learn Big Data Hadoop And Spark Developer. Big Data Hadoop And Spark Developer is a Java framework. Hadoop does not require any prior knowledge of Java. Server is an open software architecture that allows for the distributed storage and processing of very large data sets on computer clusters made of commodity hardware.

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Overview of Big Data And Spark Developer Training

The Big Data And Spark Developer Course covers Pig, Hive, and Impala, for the treatment and analysis of huge data sets stored in the HDFS and for data intake using Sqoop and Flume. Data processing will be demonstrated in real-time using Spark, covering Spark's functional programming, Spark's application implementation, parallel processing understanding in Spark, and Spark's RDD optimization strategies. In order to create and modify data formats, you will master the many interactive algorithms within Spark as well as use Spark SQL. Finally, in the areas of Banking, Telecommunications, Social Media, Insurance, and E-commerce, you are needed to implement real-life, industry-oriented projects utilizing CloudLab. You learn the big data framework through Big Data Hadoop and Spark Developer and Spark, including HDFS, YARN, and MapReduce, utilizing this Big Data Big Data Hadoop and Spark Developer course.

 

Additional Info

Future in Big Data Hadoop and Spark Developer Developer and Trending :

It offers a reliable and cost-effective data storage solution. Big Data Hadoop and Spark Developer has become a favourite of many enterprises because to its unique capabilities such as scalability and fault tolerance. Big Data Hadoop and Spark Developer, together with its ecosystem, is a solution to big data issues. Big data analytics are provided by several components of the Big Data Hadoop and Spark Developer ecosystem, such as TEZ, Mahout, Storm, MapReduce, and so on. Big Data Hadoop and Spark Developer is used by businesses to process large amounts of data. Big Data Hadoop and Spark Developer brings everything they require under one roof. Big Data Hadoop and Spark Developer solves the problems with traditional RDBMS systems. It is also less expensive than the traditional system. As a result, the Big Data Hadoop and Spark Developer market is growing at a rapid pace, and Big Data Hadoop and Spark Developer's future seems bright.


The Roles and Responsibilities of Big Data Hadoop and Spark Developer :

Companies all over the world are looking for big data professionals that can evaluate all data and generate meaningful insights. Big Data Hadoop and Spark Developer Developers can hold a variety of positions and work in a variety of settings. Here is a list of job titles that will assist you in making the best option by guiding you to the desired Big Data Hadoop and Spark Developer expert work role. Big Data Hadoop and Spark Developer employment are available in a variety of industries, including financial services, retail, banking, and healthcare.

  • To analyse the company's big data infrastructure, I met with the development team.
  • Developing and coding Big Data Hadoop and Spark Developer apps for data analysis.
  • Frameworks for data processing are being developed.
  • Data extraction and data cluster isolation.
  • Scripts are being tested and the outcomes are being analyzed.
  • Data Migration is a term used to describe the process of moving.
  • Data integration and scalability are two important factors to consider.
  • Streaming analytics is a term that refers to the study of data in speech evaluation.

The Career Opportunities of Big Data Hadoop and Spark Developer :

There is no precondition in Big Data Hadoop and Spark Developer as such for a concealed secrecy. You've got to work hard and demonstrate commitment. There are newcomers, IT industry veterans, and non-IT industries who make their careers in Big Data Hadoop and Spark Developer. Between the first phases of the job quest and offer letter, there might be much difficulty. First of all, choose the several responsibilities that Big Data Hadoop and Spark Developer must provide you on the proper path. See the different tasks of Big Data Hadoop and Spark Developer :

  • Analyst of Big Data : Big Data Analyst uses Big Data Analytics and evaluates the technological performance of organizations. And to give system enhancement recommendations. They concentrate on challenges such as live data streaming and data transfer. They work with individuals such as data scientists and data architects. This is done to make services simplified, profile source information, and establish features. Big Data Analyst performs large data operations such as parsing, text annotations, enrichment filtering.

  • Big Data Architect : The whole life of the Big Data Hadoop and Spark Developer solution is their responsibility. It involves the creation and selection of requirements, platforms, and technical architectural designs. It also includes application design and development, testing, and design of the solution offered. You should grasp the advantages and disadvantages of different technologies and platforms. They utilize cases, solutions, and suggestions to record them. To address an issue, big data must operate creatively and analytically.

  • Data Engineer : They are responsible for the creation, extent, and delivery of Big Data Hadoop and Spark Developer solutions for different large data systems. They are involved in the development of high-level architectural solutions. It manages technological communication between suppliers and domestic systems. In Kafka, Cassandra, Elasticsearch, and so forth, they manage production systems. Data Engineer constructs a club-based platform that makes new apps easy to design.

  • Data Scientist : They use their ability to compile and understand data, in terms of analytics, statistics, and program. This information will thus be used by data scientists to build data-driven solutions to complex business issues. Data Scientist works in the organization with stakeholders. This is to see how corporate data may be used to generate business solutions. Data from corporate databases are analyzed and processed. This improves the creation of products, market tactics, and company strategy.

  • Big Data Hadoop and Spark Developer developing the following : They manage Big Data Hadoop and Spark Developer installation and setup. Map-reducing code for Big Data Hadoop and Spark Developer clusters is written by Big Data Hadoop and Spark Developer Developer. They transform technical and functional difficult requirements into a comprehensive design. The software prototype testing and transmission to the operational team by the Big Data Hadoop and Spark Developer developer. The data security and privacy are maintained. They analyze and generate massive datasets.

  • Big Data Hadoop and Spark Developer tester : Big Data Hadoop and Spark Developer is the role of the tester in Big Data Hadoop and Spark Developer systems to diagnose and repair issues. It ensures that Map-Reduce work, Pig Latin, and HiveQl operate as planned. In Big Data Hadoop and Spark Developer/Hive/Pig the Big Data Hadoop and Spark Developer tester develops test cases to discover any problem. He tells the development team and manager about shortcomings and encourages them to close down. By collecting all faults, the Big Data Hadoop and Spark Developer tester generates a defect report.

  • Big Data Hadoop and Spark Developer Admin : You will work on designing, developing, and implementing C and C++ computer applications. Basically, you have to know the current technology that governs the market and design your software to match your competitors' requirements and requirements with a competitive edge over the programs that your competing organizations generateBig Data Hadoop and Spark Developer Admin is responsible for the creation, backup, and rehabilitation of a Big Data Hadoop and Spark Developer cluster. He tracks the connection and safety of the Big Data Hadoop and Spark Developer cluster. A new user has also been established. Big Data Hadoop and Spark Developer administrator handles the Big Data Hadoop and Spark Developer cluster task performance capability planning and screening. Big Data Hadoop and Spark Developer Admin supports and manages the cluster Big Data Hadoop and Spark Developer.

  • Architect of the Big Data Hadoop and Spark Developer : Big Data Hadoop and Spark Developer builds and plans the Big Data Hadoop and Spark Developer architecture for large data. Big Data Hadoop and Spark Developer. It provides the analysis of demand and selects the platform. He creates technical and application architecture. The Big Data Hadoop and Spark Developer solution offered is part of his responsibility.


Features of Big Data Hadoop and Spark Developer :

    Apache Big Data Hadoop and Spark Developer is the most popular and capable Big Data technology, providing the most dependable storage layer in the world. Let us examine the different essential characteristics of Big Data Hadoop and Spark Developer in this part.

    1. Big Data Hadoop and Spark Developer is open source :- Big Data Hadoop and Spark Developer is an open-source project, which allows companies to alter the code according to their needs, with its source code free of costs for inspection, modification, and analysis.

    2. Big Data Hadoop and Spark Developer's cluster Highly Scalable :- The Big Data Hadoop and Spark Developer cluster may be used to enhance the hardware capacity of the (vertical) nodes to obtain a large computing power by adding a variety of nodes (horizontally scalable). This offers the Big Data Hadoop and Spark Developer framework with both horizontal and vertical scalability.

    3. Fault Tolerance provided by Big Data Hadoop and Spark Developer :- The main characteristic of Big Data Hadoop and Spark Developer is fault tolerance. In Big Data Hadoop and Spark Developer 2, HDFS utilizes a fault tolerance replication method. Depending on the replication factor, each block replicates on the various computers (by default, it is 3). There are also data from the other machines with the same data if any computer in a cluster is offline. Big Data Hadoop and Spark Developer 3 substituted the erasure coding for this replication technique. Erasure coding gives less room for the same fault tolerance.

    4. Big Data Hadoop and Spark Developer delivers a high availability :- This Big Data Hadoop and Spark Developer characteristic ensures that the data is highly available even under adverse circumstances. The error tolerance feature of Big Data Hadoop and Spark Developer allows the user to access data from various DataNodes which hold a copy of the same data when any of the DataNodes goes down.

    5. Big Data Hadoop and Spark Developer is extremely affordable :- As the Big Data Hadoop and Spark Developer cluster comprises inexpensive commodities nodes, it provides an affordable option for large-scale data storage and processing. Since Big Data Hadoop and Spark Developer is open-source software, no licensing is needed.

    6. Big Data Hadoop and Spark Developer is faster in Data Processing :- Big Data Hadoop and Spark Developer holds distributed data, which enables dispersed information to be handled on a node cluster. It thereby offers the Big Data Hadoop and Spark Developer architecture with quick processing capacity.

    7. Big Data Hadoop and Spark Developer is founded on the notion of the data locality:- Big Data Hadoop and Spark Developer is well known because its data locality is the transportation of calculation logic to data, rather than the transportation of data to calculation logic. This Big Data Hadoop and Spark Developer feature lowers the use of the bandwidth in a system.

    8. Feasibility provides Big Data Hadoop and Spark Developer :- Big Data Hadoop and Spark Developer can handle unstructured data, unlike the standard system. This gives consumers the possibility to evaluate data from all sizes and formats.

    9. Big Data Hadoop and Spark Developer is easy to use :- Big Data Hadoop and Spark Developer is simple to operate since customers need not be concerned about computer distribution. The workmanship is managed through the frame.

    10. Big Data Hadoop and Spark Developer guarantees data Reliability :- Data is saved reliably on the cluster machines in Big Data Hadoop and Spark Developer despite machine failures as a result of data replication in the cluster. The frame itself offers a reliability mechanism for Block Scanners, Volume Scanners, Disk Checks, and Directory Scanners.


Top Advantages of Big Data Hadoop and Spark Developer :

Big Data Hadoop and Spark Developer is user-friendly, scalable, or economical. Big Data Hadoop and Spark Developer also provides several advantages. Here we talk about Big Data Hadoop and Spark Developer's top 12 benefits. So the positives of Big Data Hadoop and Spark Developer follow, which makes it so popular.

1. Various data sources :- Big Data Hadoop and Spark Developer takes several different data. Data may be obtained from a variety of sources such as email discussions, social media, etc. Value from different data may be derived via Big Data Hadoop and Spark Developer. The Big Data Hadoop and Spark Developer may receive information in a file with text, XML, pictures, CSV, etc.

2. Cost-effective :- Big Data Hadoop and Spark Developer is an affordable way to store data by using a commodity hardware cluster. Commodity hardware is inexpensive, thus nodes are often not too expensive to add to the framework.

3. Performance :- Big Data Hadoop and Spark Developer handles enormous volumes of high-speed data in its distributed processing and storage architecture. Even the fastest machine has been the default supercomputer. It splits the data entry file into many blocks and saves data over numerous nodes in those blocks.

4. Fault-Tolerant :- Detection coding provides for failure tolerance in Big Data Hadoop and Spark Developer 3.0. For example, with the use of an erasure coder, 6 data blocks create 3 parity blocks, which means that HDFS stores a total of nine blocks.

5. Highly available :- Big Data Hadoop and Spark Developer 2.x includes one active NomeNode architecture and one standby NameNode, so we have a backup NameNode to count on when the NameNode goes down. Big Data Hadoop and Spark Developer 3.0 offers many standby NameNode models which make the system even more readily disponible since if two or more NameNodes collapses they may continue to work.

6. Low network traffic :- Each job submitted by the user is divided into several separate subtasks in Big Data Hadoop and Spark Developer, and the data nodes are allocated to these subtasks, which transfers a small amount of code into data and does not transmit large data to code leading to low network traffic.

7. High performance :- Performance indicates work per unit time. Big Data Hadoop and Spark Developer stores data in a distributed way that makes it easy to process them distributed. A particular job is split into tiny jobs that operate concurrently to pieces of data that provide high output.

8. Open Source :- Big Data Hadoop and Spark Developer is an open-source technology, which means that its source code is available free of charge. The source code can be changed to meet a particular demand.

9. Scalable :- Big Data Hadoop and Spark Developer operates on the horizontal scalability concept, which requires that the whole computer be added to the cluster of nodes, rather than modifying the machine setup, such as adding RAMs, disc, and so on, known as vertical scalability.

10. Easy to use :- The Big Data Hadoop and Spark Developer framework is parallel to processing; programmers from MapReduce do not have to take care of the distributed processing, it is done automatically on the backdrop.

11. Compatibility :- Most new big data technologies, like Spark, Flink, etc, is Big Data Hadoop and Spark Developer compatible. You have processing engines that function as a Backend on Big Data Hadoop and Spark Developer, We utilize Big Data Hadoop and Spark Developer to store data for you.

12. Multiple languages :- Developers may code for numerous Big Data Hadoop and Spark Developer languages such as C, C++, Perl, Python, Ruby, and Groovy.


Salary of Big Data Hadoop and Spark Developer :

Job opportunities for Big Data Hadoop and Spark Developer Developers can be found in a variety of industries, including IT, finance, healthcare, retail, manufacturing, advertising, telecommunications, media & entertainment, travel, hospitality, transportation, and even government agencies. IT, e-commerce, retail, manufacturing, insurance, and finance are the six primary businesses increasing need for Big Data Hadoop and Spark Developer talent in India. E-commerce has the highest Big Data Hadoop and Spark Developer salary in India, out of all the industries. Every organization is investing in Large Data and Big Data Hadoop and Spark Developer, from big names like Amazon, Netflix, Google, and Microsoft to startups like Fractal Analytics, Sigmoid Analytics, and Crayon Data.

The compensation of the Big Data Hadoop and Spark Developer developer in India depends largely on the education credentials, credentials, work experience and the size, reputation and location of the firm. For example, postgraduate applicants can receive a start package of around Rs4–8 LPA. But graduates might earn Rs. 2.5 – 3.8 LPA for the freshers period. Professionals with the best mix of the aforementioned abilities may also earn between Rs. 5 -10 LPA anyplace. The typical yearly compensation is Rs 7 – 15 LPA to medium sized professionals with a non-management capability, while managers may perform about Rs 12 -18 LPA or higher.

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Key Features

ACTE offers Big Data Hadoop And Spark Developer Training in more than 27+ branches with expert trainers. Here are the key features,

  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.

Curriculum

Syllabus of Big Data Hadoop And Spark Developer Training
Module 1: Introduction to Linux and Big Data Virtual Machine (VM)
  • Introduction/ Installation of Virtual Box and the Big Data VM, Introduction to Linux, Why Linux?, Windows and the Linux equivalents, Different flavors of Linux, Unity Shell (Ubuntu UI), Basic Linux Commands (enough to get started with Hadoop)
Module 2: Understanding Big Data
  • 3V (Volume- Variety- Velocity) characteristics
  • Structured and unstructured data
  • Application and use cases of Big Data
  • Limitations of traditional large scale systems
  • How a distributed way of computing is superior (cost and scale)
  • Opportunities and challenges with Big Data
Module 3: HDFS (The Hadoop Distributed File System)
  • HDFS Overview and Architecture
  • Deployment Architecture
  • Name Node
  • Data Node and Checkpoint Node (aka Secondary Name Node)
  • Safe mode
  • Configuration files
  • HDFS Data Flows (Read/Write)
Module 4: How HDFS Addresses Fault Tolerance?
  • CRC Checksum
  • Data Replication
  • Rack awareness and block placement policy
  • Small file problems
Module 5: HDFS Interfaces
  • Command-Line Interface
  • File Systems
  • Administrative
  • Web Interfaces
Module 6: Advanced HDFS Features
  • Load Balancer
  • Dist cp (Distributed Copy)
  • HDFS Federation
  • HDFS High Availability
  • Hadoop Archives
Module 7: Map Reduce – 1 (Theoretical Concepts)
  • MapReduce overview
  • Functional Programming paradigms
  • How to think in a MapReduce way
Module 8: MapReduce Architecture
  • Legacy MR v/s Next Generation MapReduce (YARN/ MRv2)
  • Slots v/s Containers
  • Schedulers
  • Shuffling, Sorting
  • Hadoop Data Types
  • Input and Output Formats
  • Input Splits – Partitioning (Hash Partitioner v/s Customer Partitioner)
  • Configuration files
  • Distributed Cache
Module 9: MR Algorithm and Data Flow
  • Word Count
Module 10: Alternatives to MR – BSP (Bulk Synchronous Parallel)
  • Adhoc Querying
  • Graph Computing Engines
Module 11: MapReduce – 2 (Practice) Developing, Debugging and Deploying MR Programs
  • Standalone mode (in Eclipse)
  • Pseudo Distributed mode (as in the Big Data VM)
  • Fully Distributed mode (as in Production)
  • MR API
  • Old and the New MR API
  • Java Client API
  • Hadoop data types
  • Custom Writable
Module 12: WritableComparable
  • Different input and output formats
  • Saving Binary Data using Sequence Files and Avro Files
  • Hadoop Streaming (developing and debugging non Java MR programs – Ruby and Python)
Module 13: Optimization Techniques
  • Speculative execution
  • Combiners
  • JVM Reuse
  • Compression
Module 14: Mr Algorithms (Non- Graph)
  • Sorting
  • Term Frequency
  • Inverse Document Frequency
  • Student Database
  • Max Temperature
  • Different ways of joining data
  • Word Co-occurrence
Module 15: Proof of Concepts and Use Cases
  • Click Stream Analysis using Pig and Hive
  • Analyzing the Twitter data with Hive
  • Further ideas for data analysis
Module 16: Advance HBase Features
  • HBase Data Modeling
  • Bulk loading data in HBase
  • HBase Coprocessors – Endpoints (similar to Stored Procedures in RDBMS)
  • HBase Coprocessors – Observers (similar to Triggers in RDBMS)
Module 17: MR Algorithms (Graph)
  • PageRank
  • Inverted Index
Module 18: Higher Level Abstractions for MR (Pig)
  • Introduction and Architecture
  • Different modes of executing Pig constructs
  • Data Types
  • Dynamic Invokers
  • Pig streaming Macros
  • Pig Latin language Constructs (LOAD, STORE, DUMP, SPLIT, etc)
  • User-Defined Functions
  • Use Cases
Module 19: Comparison of Pig and Hive
  • NoSQL Databases – 1 (Theoretical Concepts)
  • NoSQL Concepts
  • Review of RDBMS
  • Need for NoSQL
  • Brewers CAP Theorem
  • ACI D v/s BASE
  • Schema on Read vs. Schema on Write
  • Different levels of consistency
  • Bloom filters
Module 20: Columnar Databases Concepts NoSQL Databases – 2 (Practice)
  • HBase Architecture
  • Master and the Region Server
  • Catalog tables (ROOT and META)
  • Major and Minor Compaction
  • Configuration Files
  • HBase v/s Cassandra
Module 21: Interfaces to HBase (for DDL and DML Operations)
  • Java API
  • Client API
  • Filters
  • Scan Caching and Batching
  • Command Line Interface
  • REST API
Module 22: Introduction to Sqoop
  • Use-case of Sqoop
  • Sqoop Architecture
  • Sqoop Demo
Module 23: Introduction to Flume
  • Use-case of Flume
  • Flume Architecture
  • Flume Demo
Module 24: Introduction to Oozie
  • Use-case of Oozie
  • Oozie Architecture
  • Oozie Demo
Module 25: Introduction to Yarn
  • Usecase of YARN
  • YARN Architecture
  • YARN Demo
Module 26: Spark
  • Introduction to RDD
  • Installation and Configuration of Spark
  • Spark Architecture
  • Different interfaces to Spark
  • Sample Python programs in Spark
Module 27: Hadoop Ecosystem and Use Cases
  • Hadoop industry solutions
  • Importing/exporting data across RDBMS and HDFS using Sqoop
  • Getting real-time events into HDFS using Flume
  • Creating workflows in Oozie
  • Introduction to Graph processing
  • Graph processing with Neo4J
  • Using the Mongo Document Database
  • Using the Cassandra Columnar Database
  • Distributed Coordination with Zookeeper
Module 28: SSH Configuration
  • Stand alone mode (Theory)
  • Distributed mode (Theory)
  • Pseudo distributed
  • Fully-distributed
Module 29: Setting up a Hadoop Cluster using Apache Hadoop
  • Cloudera Hadoop cluster on the Amazon Cloud (Practice)
  • Using EMR (Elastic Map Reduce)
  • Using EC2 (Elastic Compute Cloud)
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Get Hands-on Knowledge about Real-Time Big Data Hadoop And Spark Developer Projects

Project 1
Electricity price forecasting

This Project gives real-time records of load and rate for the higher prediction of power technology purposes.

Project 2
Health status prediction.

The aim of this undertaking become to apply a middle NHS records set, Hospital Episode, adjustment fashions to predict.

Project 3
Anomaly detection in cloud servers

The aim was to construct an set of rules that determines if a contribution has a threat to be misguided coded.

Project 4
Malicious user detection in Big Data collection

The motive of malware evaluation is to gain and offer the facts had to rectify a community or device intrusion.

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  • We offer a Big Data Hadoop And Spark Developer and preparation based on leading industrial companies with a 100% full guarantee.
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  • Overall, we will ensure that our applicants stay engaged and that their overall learning experience is adaptable, helpful, and helpful.
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  • Our Big Data Hadoop And Spark Developer Training trainers understand the importance of the characteristics of the best exceptional Big Data Hadoop And Spark Developer training and distinguish the correct preparation techniques with regard to the applicant's profile and reason.
  • The instructors are guaranteed to be talented experts with over 9 years of experience in their respective fields.
  • The involvement of our mentor in the preparation enabled our applicants to be guaranteed experts in Big Data Hadoop And Spark Developer training.
  • Instructors are fully trained in their respective areas of work and have the potential and skills to convey your content.
  • By providing meaningful insight into in-depth questions and conducting meetings through newly created interviews, our tutors help applicants create an expert resume and increase its accuracy.
  • To ensure the absolute satisfaction of our candidates, our mentors have conducted an end-to-end course tailored to your needs and working standards.

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Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    ACTE Gives Certificate For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Big Data Hadoop And Spark Developer training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Big Data Hadoop And Spark Developer Course At ACTE?

  • Big Data Hadoop And Spark Developer Course in ACTE is designed & conducted by Big Data Hadoop And Spark Developer experts with 10+ years of experience in the Big Data Hadoop And Spark Developer domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Big Data Hadoop And Spark Developer batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Big Data Hadoop And Spark Developer . 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.
You can contact our support number at +91 93800 99996 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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      Job Opportunities in Big Data

      More than 35% of Data Professionals Prefer Big Data. Big Data Is Widely Recognized as the Most Popular and In-demand Data Technology in the Tech World.

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