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Our Big Data course in Seattle is curated by industry experts for you to have a clear understanding of Big Data Analytics tools—Hadoop cluster, Scala, MapReduce, HDFS, Hive, Pig, etc.—to clear the certification exam. As part of this best Hadoop Training in Seattle, you will also get free upgrades of the courseware and 24/7 tech support.
This Big Data Hadoop training in Seattle at Intellipaat hones the necessary skill sets, such as basics of Big Data Hadoop, Hadoop ecosystem components, Spark SQL, Machine Learning using Spark, etc., that are required for a Big Data expert.
In this Big Data training program in Seattle, you will learn the following concepts from the experts at Intellipaat:
Below are a few reasons why you must take up this Big Data online training course in Seattle:
Our Big Data online training course is helpful for the below-mentioned professionals:
No prior knowledge is required to learn from our Big Data training course in Seattle.
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Senior Software Engineer | Gurgaon
Senior Software Engineer
Big Data Professional | India
Intellipaat has provided me with great content as per my requirement to shift from Software Engineering to Big Data. I recommend their courses to everyone who wishes to aim for a successful career transition. Kudos to the team!
Senior Software Engineer
Big Data Professional
Big Data Expert | India
This course has helped me make a smooth career transition from a non-tech background to a Big Data Expert. My objective of gaining skills in data driven decision making after my MBA was fulfilled. Also, great career guidance.
Big Data Expert
Data Scientist | India
Becoming a Data Scientist from a Customer Service Agent has been possible only due to Intellipaat and the expert guidance by its trainers. Even after working for 10 years in customer care, I am a Data scientist today.
Customer Service Agent
Data Scientist | Delhi
After working in non-tech, I wanted to shift to Data Science but it seemed difficult. Thanks to Intellipaat’s rich training, I am a successful Data Scientist. Post the training, I believe Data Science is for non-tech too.
Digital Marketing Analyst | India
Post the training, I was able to shift from a Data Steward Specialist to an Analyst with a 35% salary hike. I gained a deep understanding of technical skills, especially in analytics. I can’t thank you enough, Intellipaat.
Data Steward Specialist
Digital Marketing Analyst
Big Data Developer | Dallas
The training helped me make a career transition from Computer Technical Specialist to Big Data developer with a 60% hike. The online sessions are engaging like any physical class and the trainers make the sessions interactive.
Computer Technical Specialist
Big Data Developer
Data Engineer | Pune
Program Manager | Pune
Post this program, I was able to switch to the role of a Program Manager from a Microsoft Dynamics consultant. Gaining knowledge in the latest technologies as per industry standards, helped me in this career transition.
Microsoft Dynamics Consultant
ETL Developer | Maharashtra
Thanks to Intellipaat I was able to make a transition from Consultant to ETL Developer. The rich content has helped me get to this role. I am extremely satisfied with my career today. This is the best online training so far.
Splunk Administrator | Bangalore
I was a non-IT person before enrolling in the course. But I could make a transition to a Support Executive at IBM, all because of Intellipaat’s comprehensive content, expert trainers, and a great job assistance team.
57% Average Salary Hike
$1,28,000 Highest Salary
12000+ Career Transitions
300+ Hiring Partners
Self Paced Training
Online Classroom Preferred
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
13.1 Understanding the concept of key-value pair in RDDs
13.2 Learning how Spark makes MapReduce operations faster
13.3 Various operations of RDD
13.4 MapReduce interactive operations
13.5 Fine and coarse-grained update
13.6 Spark stack
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
21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems
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
30.1 Report defects to the development team or manager and driving them to closure
30.2 Consolidate all the defects and create defect reports
30.3 Responsible for creating a testing framework called MRUnit for testing of MapReduce programs
Module 31 - Unit TestingPreview
31.1 Automation testing using the OOZIE
31.2 Data validation using the query surge tool
Module 32 - Test ExecutionPreview
32.1 Test plan for HDFS upgrade
32.2 Test automation and result
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Working with MapReduce, Hive, and Sqoop
In this project, the learners import MySQL data with the help of Sqoop. As an important requirement of the project, the learners are required to query the same, by using Hive, and run the word count with the use of MapReduce.
Work on MovieLens Data For Finding the Top Movies
This project involves writing a MapReduce program to analyze the MovieLens data. The project also involves creating a list of top 10 movies by using Apache Pig and Apache Hive for working with distributed datasets.
Hadoop YARN Project: End-to-End PoC
The Hadoop YARN project lets the learners import daily incremental data in HDFS. The project allows the learners to use Sqoop commands to import this data and also work with end-to-end data transaction flow and HDFS data.
Table Partitioning in Hive
It improves query speed by using Hive data partitioning. It also helps in getting experience on manual Hive tables partition and deploying single SQL execution in dynamic partitioning and bucket data to break it into managable chunks.
Connecting Pentaho with Hadoop Ecosystem
Learning to deploy ETL for data analysis activities, getting a chance to configure Pentaho, and working with Hadoop distribution. The learners also get hands-on experience to load, transform, and extract data from the Hadoop cluster.
Multi-node Cluster Setup
Set up a Hadoop real-time cluster on Amazon EC2. Install and configure Hadoop. Run a Hadoop multinode by using a 4-node cluster on Amazon EC2 and deploy a MapReduce job on the Hadoop cluster. Java installed is a prerequisite.
Hadoop Testing Using MRUnit
Work with MRUnit to test the Hadoop application in isolation without spinning a cluster. The learners are also required to successfully map and reduce the tests in an application, as an important requirement of the project.
Hadoop Web Log Analytics
The Hadoop Web Log Analytics project requires the learners to successfully derive insights from web log data. Aggregate log data and implement Apache Flume for data transportation. Also process the data to generate analytics.
Through this project, the learners will grasp how to administer a Hadoop cluster to maintain and manage it. Work with the name node directory structure, audit logging, data node block scanner, Hadoop file formats, etc.
Twitter Sentiment Analysis
Use and successfully apply Twitter sentiment analysis to find the reaction of people concerning the demonetization move in India by analyzing their tweets. The learners can also download the tweets and load them into Pig storage.
Analyzing IPL T20 Cricket
This interesting project has been included to let the learners analyze an IPL T20 cricket match and get some details of the match. The next step is to load the IPL dataset into HDFS and analyze the data using Apache Pig or Hive.
Recommend the best movie based on the user's taste. This hands-on Apache Spark project, along with using the Apache Spark MLlib, includes the creation of collaborative filtering, regression, clustering, and dimensionality reduction.
Twitter API Integration for Tweet Analysis
This project facilitates learning to analyze the sentiments of the user by a tweet. As a part of the project, the learners will be required to successfully integrate Twitter API and utilize PHP or Python to build a server-side code.
Data Exploration Using Spark SQL – Wikipedia Data Set
This project has been included to help the learners to combine Spark SQL with ETL applications, perform real-time data analysis, deploy machine learning algorithms, perform batch analysis, build visualizations, and process graphs.
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 training course is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exams. The entire training course content is in line with these certification programs and helps you clear these certification exams with ease and get the best jobs in the top MNCs.
As part of this Big Data Course in Seattle, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.
At the end of this Big Data Courses in Seattle, there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and help you score better.
Intellipaat Course Completion Certificate will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
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 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!
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 online course and continue to be a member.
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.
It is a known fact that the demand for Hadoop professionals far outstrips the supply. So, if you want to learn and make a career in Hadoop, then you need to enroll for Intellipaat Hadoop course which is the most recognized name in Hadoop training and certification. Intellipaat Hadoop training includes all major components of Big Data and Hadoop like Apache Spark, MapReduce, HBase, HDFS, Pig, Sqoop, Flume, Oozie and more. The entire Intellipaat’s Big Data Training in Seattle has been created by industry professionals. You will get 24/7 lifetime support, high-quality course material and videos and free upgrade to latest version of course material. Thus, it is clearly a one-time investment for a lifetime of benefits.
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 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of 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 support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.
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.