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Our Big Data course in Mountain View is curated by SMEs who will assist you in gaining mastery in the various concepts of Big Data and Hadoop, such as Hive, Pig, Oozie, Flume, MapReduce, RDDs, Scala, etc. As part of this best Hadoop training in Mountain View, you will work on real-time projects and will get 24/7 lifetime support from us.
Intellipaat’s Big Data Hadoop Training in Mountain View covers all the core concepts of Big Data Analytics, including Hadoop, YARN, HDFS, MapReduce, Spark, Spark SQL, working with Avro data formats, Spark Streaming, Impala, HCatalog, and so on, through projects and assignments.
Our trainers will teach you the following key concepts of Big Data Hadoop:
Intellipaat’s Big Data online training is for:
There are no skills required to join this Big Data training program in Mountain View.
There are several reasons to learn Big Data Hadoop in Mountain View. Here, we have listed a few:
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Big Data Developer | Dallas
This is a great training program. The mentors and course instructors were interactive throughout the program. This course has helped me upskill myself in the numerous tools and technologies that work with Big Data. The experts hel...Read More
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I had a great experience in learning from Intellipaat and it allowed me to gain the skills to be a Software Engineer from an Associate Consultant. The course instructors here helped me master the tools and techniques in the field....Read More
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ETL Developer | Maharashtra
It is the best online course I have ever taken. The course instructors here helped me gain in-depth knowledge of all the topics that could help me shift my career from a Consultant to an ETL Developer. To get into this domain, I r...Read More
Splunk Administrator | Bangalore
I had a little knowledge in the field of IT and was working as a Support Executive in IBM. I learned the various concepts and techniques involved in Splunk through Intellipaat’s training which helped me move on to a career in this field and land a lucrative job.
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Module 01 - Hadoop Installation and SetupPreview
1.1 The architecture of Hadoop cluster
1.2 What is High Availability and Federation?
1.3 How to setup a production cluster?
1.4 Various shell commands in Hadoop
1.5 Understanding configuration files in Hadoop
1.6 Installing a single node cluster with Cloudera Manager
1.7 Understanding Spark, Scala, Sqoop, Pig, and Flume
Module 02 - Introduction to Big Data Hadoop and Understanding HDFS and MapReducePreview
2.1 Introducing Big Data and Hadoop
2.2 What is Big Data and where does Hadoop fit in?
2.3 Two important Hadoop ecosystem components, namely, MapReduce and HDFS
2.4 In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager
1. HDFS working mechanism
2. Data replication process
3. How to determine the size of the block?
4. Understanding a data node and name node
Module 03 - Deep Dive in MapReducePreview
3.1 Learning the working mechanism of MapReduce
3.2 Understanding the mapping and reducing stages in MR
3.3 Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort
1. How to write a WordCount program in MapReduce?
2. How to write a Custom Partitioner?
3. What is a MapReduce Combiner?
4. How to run a job in a local job runner
5. Deploying a unit test
6. What is a map side join and reduce side join?
7. What is a tool runner?
8. How to use counters, dataset joining with map side, and reduce side joins?
Module 04 - Introduction to HivePreview
4.1 Introducing Hadoop Hive
4.2 Detailed architecture of Hive
4.3 Comparing Hive with Pig and RDBMS
4.4 Working with Hive Query Language
4.5 Creation of a database, table, group by and other clauses
4.6 Various types of Hive tables, HCatalog
4.7 Storing the Hive Results, Hive partitioning, and Buckets
1. Database creation in Hive
2. Dropping a database
3. Hive table creation
4. How to change the database?
5. Data loading
6. Dropping and altering table
7. Pulling data by writing Hive queries with filter conditions
8. Table partitioning in Hive
9. What is a group by clause?
Module 05 - Advanced Hive and ImpalaPreview
5.1 Indexing in Hive
5.2 The ap Side Join in Hive
5.3 Working with complex data types
5.4 The Hive user-defined functions
5.5 Introduction to Impala
5.6 Comparing Hive with Impala
5.7 The detailed architecture of Impala
1. How to work with Hive queries?
2. The process of joining the table and writing indexes
3. External table and sequence table deployment
4. Data storage in a different table
Module 06 - Introduction to PigPreview
6.1 Apache Pig introduction and its various features
6.2 Various data types and schema in Hive
6.3 The available functions in Pig, Hive Bags, Tuples, and Fields
1. Working with Pig in MapReduce and local mode
2. Loading of data
3. Limiting data to 4 rows
4. Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive
Module 07 - Flume, Sqoop and HBasePreview
7.1 Apache Sqoop introduction
7.2 Importing and exporting data
7.3 Performance improvement with Sqoop
7.4 Sqoop limitations
7.5 Introduction to Flume and understanding the architecture of Flume
7.6 What is HBase and the CAP theorem?
1. Working with Flume to generate Sequence Number and consume it
2. Using the Flume Agent to consume the Twitter data
3. Using AVRO to create Hive Table
4. AVRO with Pig
5. Creating Table in HBase
6. Deploying Disable, Scan, and Enable Table
Module 08 - Writing Spark Applications Using ScalaPreview
8.1 Using Scala for writing Apache Spark applications
8.2 Detailed study of Scala
8.3 The need for Scala
8.4 The concept of object-oriented programming
8.5 Executing the Scala code
8.6 Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
8.7 The Java and Scala interoperability
8.8 The concept of functional programming and anonymous functions
8.9 Bobsrockets package and comparing the mutable and immutable collections
8.10 Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.
1. Writing Spark application using Scala
2. Understanding the robustness of Scala for Spark real-time analytics operation
Module 09 - Use Case Bobsrockets PackagePreview
9.1 Introduction to Scala packages and imports
9.2 The selective imports
9.3 The Scala test classes
9.4 Introduction to JUnit test class
9.5 JUnit interface via JUnit 3 suite for Scala test
9.6 Packaging of Scala applications in the directory structure
9.7 Examples of Spark Split and Spark Scala
Module 10 - Introduction to SparkPreview
10.1 Introduction to Spark
10.2 Spark overcomes the drawbacks of working on MapReduce
10.3 Understanding in-memory MapReduce
10.4 Interactive operations on MapReduce
10.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
10.6 The overview of Spark and how it is better than Hadoop
10.7 Deploying Spark without Hadoop
10.8 Spark history server and Cloudera distribution
Module 11 - Spark BasicsPreview
11.1 Spark installation guide
11.2 Spark configuration
11.3 Memory management
11.4 Executor memory vs. driver memory
11.5 Working with Spark Shell
11.6 The concept of resilient distributed datasets (RDD)
11.7 Learning to do functional programming in Spark
11.8 The architecture of Spark
Module 12 - Working with RDDs in SparkPreview
12.1 Spark RDD
12.2 Creating RDDs
12.3 RDD partitioning
12.4 Operations and transformation in RDD
12.5 Deep dive into Spark RDDs
12.6 The RDD general operations
12.7 Read-only partitioned collection of records
12.8 Using the concept of RDD for faster and efficient data processing
12.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
Module 13 - Aggregating Data with Pair RDDsPreview
Module 14 - Writing and Deploying Spark ApplicationsPreview
14.1 Comparing the Spark applications with Spark Shell
14.2 Creating a Spark application using Scala or Java
14.3 Deploying a Spark application
14.4 Scala built application
14.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
14.6 Creating an application using SBT
14.7 Deploying an application using Maven
14.8 The web user interface of Spark application
14.9 A real-world example of Spark
14.10 Configuring of Spark
Module 15 - Project Solution Discussion and Cloudera Certification Tips and TricksPreview
15.1 Working towards the solution of the Hadoop project solution
15.2 Its problem statements and the possible solution outcomes
15.3 Preparing for the Cloudera certifications
15.4 Points to focus on scoring the highest marks
15.5 Tips for cracking Hadoop interview questions
1. The project of a real-world high value Big Data Hadoop application
2. Getting the right solution based on the criteria set by the Intellipaat team
Module 16 - Parallel ProcessingPreview
16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions
Module 17 - Spark RDD PersistencePreview
17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
Module 18 - Spark MLlibPreview
18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
1. Building a Recommendation Engine
Module 19 - Integrating Apache Flume and Apache KafkaPreview
19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka
1. Configuring Single Node Single Broker Cluster
2. Configuring Single Node Multi Broker Cluster
3. Producing and consuming messages
4. Integrating Apache Flume and Apache Kafka
Module 20 - Spark StreamingPreview
20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators
1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming
Module 21 - Improving Spark PerformancePreview
Module 22 - Spark SQL and Data FramesPreview
22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine
Module 23 - Scheduling/PartitioningPreview
23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions
Module 24 - Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2Preview
24.1 Create a 4-node Hadoop cluster setup
24.2 Running the MapReduce Jobs on the Hadoop cluster
24.3 Successfully running the MapReduce code
24.4 Working with the Cloudera Manager setup
1. The method to build a multi-node Hadoop cluster using an Amazon EC2 instance
2. Working with the Cloudera Manager
Module 25 - Hadoop Administration – Cluster ConfigurationPreview
25.1 Overview of Hadoop configuration
25.2 The importance of Hadoop configuration file
25.3 The various parameters and values of configuration
25.4 The HDFS parameters and MapReduce parameters
25.5 Setting up the Hadoop environment
25.6 The Include and Exclude configuration files
25.7 The administration and maintenance of name node, data node directory structures, and files
25.8 What is a File system image?
25.9 Understanding Edit log
1. The process of performance tuning in MapReduce
Module 26 - Hadoop Administration – Maintenance, Monitoring and TroubleshootingPreview
26.1 Introduction to the checkpoint procedure, name node failure
26.2 How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes
1. How to go about ensuring the MapReduce File System Recovery for different scenarios
2. JMX monitoring of the Hadoop cluster
3. How to use the logs and stack traces for monitoring and troubleshooting
4. Using the Job Scheduler for scheduling jobs in the same cluster
5. Getting the MapReduce job submission flow
6. FIFO schedule
7. Getting to know the Fair Scheduler and its configuration
Module 27 - ETL Connectivity with Hadoop Ecosystem (Self-Paced)Preview
27.1 How ETL tools work in Big Data industry?
27.2 Introduction to ETL and data warehousing
27.3 Working with prominent use cases of Big Data in ETL industry
27.4 End-to-end ETL PoC showing Big Data integration with ETL tool
1. Connecting to HDFS from ETL tool
2. Moving data from Local system to HDFS
3. Moving data from DBMS to HDFS,
4. Working with Hive with ETL Tool
5. Creating MapReduce job in ETL tool
Module 28 - Hadoop Application TestingPreview
28.1 Importance of testing
28.2 Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end-to-end tests, Functional testing, Release certification testing, Security testing, Scalability testing, Commissioning and Decommissioning of data nodes testing, Reliability testing, and Release testing
Module 29 - Roles and Responsibilities of Hadoop Testing ProfessionalPreview
29.1 Understanding the Requirement
29.2 Preparation of the Testing Estimation
29.3 Test Cases, Test Data, Test Bed Creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, Hive and HBase) while loading the input (logs, files, records, etc.) using Sqoop/Flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges, etc.), reporting defects to the development team or manager and driving them to closure
29.4 Consolidating all the defects and create defect reports
29.5 Validating new feature and issues in Core Hadoop
Module 30 - Framework Called MRUnit for Testing of MapReduce ProgramsPreview
Module 31 - Unit TestingPreview
Module 32 - Test ExecutionPreview
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Working with MapReduce, Hive, and Sqoop
In this project, you will successfully import data using Sqoop into HDFS for data analysis. The transfer will be from Sqoop data transfer from RDBMS to Hadoop. You will code in Hive query language and carry out data querying and analy...Read More
Work on MovieLens Data For Finding the Top Movies
Create the top-ten-movies list using the MovieLens data. For this project, you will use the MapReduce program for working on the data file, Apache Pig for analyzing data, and Apache Hive data warehousing and querying. You will be working with distributed datasets.
Hadoop YARN Project: End-to-End PoC
Bring the daily incremental data into the Hadoop Distributed File System. As part of the project, you will be using Sqoop commands to bring the data into HDFS, working with the end-to-end flow of transaction data, and the data from HD...Read More
Table Partitioning in Hive
In this project, you will learn how to improve the query speed using Hive data partitioning. You will get hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, and bucketi...Read More
Connecting Pentaho with Hadoop Ecosystem
Deploy ETL for data analysis activities. In this project, you will challenge your working knowledge of ETL and Business Intelligence. You will configure Pentaho to work with Hadoop distribution as well as load, transform, and extract data into the Hadoop cluster.
Multi-node Cluster Setup
Set up a Hadoop real-time cluster on Amazon EC2. The project will involve installing and configuring Hadoop. You will need to run a Hadoop multi-node using a 4-node cluster on Amazon EC2 and deploy a MapReduce job on the Hadoop cluste...Read More
Hadoop Testing Using MRUnit
In this project, you will be required to test MapReduce applications. You will write JUnit tests using MRUnit for MapReduce applications. You will also be doing mock static methods using PowerMock and Mockito and implementing MapRedu...Read More
Hadoop Web Log Analytics
Derive insights from web log data. The project involves the aggregation of log data, implementation of Apache Flume for data transportation, and processing of data and generating analytics. You will learn to use workflow and data cleansing using MapReduce, Pig, or Spark.
Through this project, you will learn how to administer a Hadoop cluster for maintaining and managing it. You will be working with the name node directory structure, audit logging, data node block scanner, balancer, Failover, fencing, DISTCP, and Hadoop file formats.
Twitter Sentiment Analysis
Find out what is the reaction of the people to the demonetization move by India by analyzing their tweets. You will have to download the tweets, load them into Pig storage, divide the tweets into words to calculate sentiment, rate the...Read More
Analyzing IPL T20 Cricket
This project will require you to analyze an entire cricket match and get any details of the match. You will need to load the IPL dataset into HDFS. You will then analyze that data using Apache Pig or Hive. Based on the user queries, the system will have to give the right output.
Recommend the most appropriate movie to a user based on his taste. This is a hands-on Apache Spark project, which will include the creation of collaborative filtering, regression, clustering, and dimensionality reduction. You will nee...Read More
Twitter API Integration for Tweet Analysis
Analyze the user sentiment based on a tweet. In this Twitter analysis project, you will integrate the Twitter API and use Python or PHP for developing the essential server-side codes. You will carry out filtering, parsing, and aggrega...Read More
Data Exploration Using Spark SQL – Wikipedia Data Set
In this project, you will be making use of the Spark SQL tool for analyzing Wikipedia data. You will be integrating Spark SQL for batch analysis, Machine Learning, visualizing, and processing of data and ETL processes, along with real-time analysis of data.
Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!
This Big Data online training course in Mountain View is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exam. The entire Hadoop training course is in line with this exam and helps you clear it with ease and get placed in top MNCs.
As part of this Big Data Course in Mountain View, you will be working on real-time projects and assignments that have immense implications in real-world industry scenarios, thus helping you fast-track your career effortlessly.
At the end of this Big Data Courses in Mountain View, there will be quizzes that perfectly reflect the type of questions asked in the certification exam to help you score better.
Intellipaat’s Hadoop training course completion certificate will be awarded once you complete the project work (after review) and score a minimum of 60 percent in the quiz. This certification is well-recognized by top 80+ MNCs, including Hexaware, Ericsson, Mu Sigma, TCS, Genpact, Saint-Gobain, Cisco, Sony, Cognizant, Standard Chartered, etc.
I'm really thankful to Intellipaat about the Hadoop Architect Course with Big Data certification. First of all, the team supported me in finding the best Big Data online course based on my experiences...Read More
Best platform to master latest technologies This is the ultimate platform to learn any course. I am highly impressed with their training program and would recommend Intellipaat to everyone looking for a course.
A big data Hadoop online training course that hits the bull's eye. The Hadoop trainer was a master of big data and Hadoop concepts and implementation. Great to learn at Intellipaat!
This is regards to conveying my deepest gratitude to Intellipaat. The quality and methodology of the online Hadoop training is matchless. The self-study program for which I had enrolled for Big Data H...Read More
I wish I knew about Intellipaat online Hadoop training before. I have hugely benefitted from this big data Hadoop certification course. Excellent course material and highly recommended Hadoop trainers...Read More
This online big data Hadoop training is extremely industry-focused and job-oriented. Overall I am giving 10 out of 10 for this Hadoop certification course from Intellipaat!
I mastered Hadoop through the Intellipaat Big Data Hadoop online training. Let me frankly tell you that this course is designed in a unique and comprehensive manner that is by far the best. Plus you g...Read More
A big thank you to the entire Intellipaat Big Data Hadoop Team! You have delivered a great Hadoop online certification training course, with equally informative Hadoop online tutorials, Big Data video...Read More
This Intellipaat Hadoop tutorial has delivered more than what they had promised to me. Since I have undergone previous Hadoop Course training I am quite familiar with Big Data Hadoop concepts but Inte...Read More
Thank you very much for your training. The trainer resolved my query in record time and that too as per my utmost sanctification. I have no words to describe my gratitude to Intellipaat.
Recently I completed Big Data Hadoop Certification Training from intellipaat. Great Learning. The best investment I ever made in my career. I've learnt and benefitted a lot from intellipaat big data o...Read More
I had taken Intellipaat Big Data Hadoop Online. An excellent online mode of learning. Now I am confident and able to look out for a career in Big Data. upon successfully completing this course Thanks ...Read More
I wanted to learn Big Data since it had a huge scope. My career changed positively upon completion of Intellipaat Big Data Training. Go with Intellipaat for a Bright Career !!! Thanks.
There are more job opportunities in Big Data Analytics when compared to the previous years, and more IT professionals are eager to take up Big Data Hadoop training to excel in the domain. Intellipaat’s Big Data Hadoop course is the most recognized one in Hadoop training and certification. Skilled trainers at Intellipaat will walk you through the major components of Big Data and Hadoop , such as HBase, Spark, MapReduce, Pig, HDFS, Sqoop, Flume, Oozie, etc. Once you enroll in this Big Data training in Mountain View, you will get 24/7 lifetime support, high-quality course material and videos, and much more. Intellipaat also offers job assistance to its students.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.