Introduction to Big Data
The growing speed of data databases creates serious problems in effective data management. For the first time, Big Data technologies work with Google File System multiple times and create opportunities to use the power of multiple servers in parallel. This can create conditions for a complete solution to the problem experienced by analysts in relation to big data. In today's world, all big companies are trying to build a Big Data Ecosystem, and soon all the data will be allowed to accumulate in Data Lakes, which can be used for your influence methods. Big Data training will bring you up-to-date with modern computing technologies. Big Data training is a great option to start Big Data Science. In this training, you will have the opportunity to understand the essence of dozens of the most important Big Data tools that have resulted in parallel processing of data in the market.
Training table
Big Data
Big Data
Big Data
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Information about the training
During the training, you will be introduced to the use of a number of tools including the Apache Hadoop architecture, HDFS, HBase, Spark, Yarn, Apache Spark and MapReduce. They will also master Spark's RDDs, datasets, optimizing SparkSQL, and working with Spark's development and runtime environment options, including the basics of parallel programming with SparkSQL for DataFrames and Data.
Who is this training for?
Employees of companies experiencing a Big Data problem,
Professionals who apply Data Science and want to perform analyzes on Big Data, those who want to develop themselves in the field of Data Science, programmers who want to learn Deep learning executives and specialists who do not have knowledge and experience in this field but want to be informed about the possibilities of Big Data and programmers and data engineers.
Certificate
Those who successfully complete the training will receive a Certified Big Data for Data Science certificate and others will receive a participation certificate. You can see a sample certificate on the right.
Big Data in 5 minutes
What Is Big Data? Big Data Analytics, Big Data Tutorial.
Simplilearn
This video, Big Data In 5 Minutesby Simplilearn, will help you understand what is Big Data, the 5 V's of Big Data, why Hadoop came into existence, and what Hadoop is.
Syllabus
Session 1
- Operating Systems and Linux (Ubuntu)
- What is Version Control and Git?
- Managing Projects with GitHub
Session 2
- Introduction to Big Data
- Introduction to Hadoop
- Hadoop Distributed File System (HDFS)
- MapReduce Process with MrJob Library
Case Study 1
MapReduce of Data with MrJob Library.
Session 3
- Hadoop Ecosystem - Hive, Pig, HBase
- Hadoop Ecosystem - Zookeeper, YARN, Ambari
- Applying MapReduce Code on Hadoop Ecosystem
Case Study 2
MapReduce Process in the Hadoop Ecosystem, working with real airport data.
Session 4
- Introduction to Spark
- RDD-based Programming
- Transformations and Actions
- Datasets and Databases
- Introduction to SparkSQL and Spark Structured Streaming
Case Study 3
Building Machine Learning Models and Measuring Model Performance using PySpark.
Session 5
- Cloud Computing for Big Data: Introduction to AWS Practical and AWS EC2, and Other AWS Services
- NoSQL Databases and MongoDB
Case Study 4
- Creating a Virtual System on an AWS Server with Python.
- Managing NoSQL Databases with PyMongo.
Trainers
Cəlal Rəhmanov
Data Elmi üzrə Ekspert, Kapital bank ASCWith over 4 years of experience in the field of data science, Jalal Rahmanov currently serves as a Data Science Expert at Kapital Bank’s Micro Business Tribe. He was previously a Data Scientist specializing in NLP and AI at Kapital Bank’s Center of Excellence team, where he contributed to international projects presented by foreign experts in European countries such as Germany.
Prior to this, he held the position of CVM and BI - Junior Data Scientist at Yelo Bank. Jalal has both onsite and remote work experience with local and international companies, including Azerbaijan Artificial Intelligence Laboratory, Pasha Bank, and The Sparks Foundation.
He possesses practical skills in the implementation and integration of technologies such as Python, Tableau, Dataiku, SQL, GitLab, Docker, SparkMLlib, and Kafka.