The success of many enterprises including banking in future is highly dependent on smart uses of big data technologies, in this regard big data training for banking enterprises is an essential tool to make entry towards big data technologies. The trends for adopting big data technologies over traditional relational databases/data warehouses for data analytics is rapidly surging up world wide. Among multiple facets of Big data application areas, banking sectors are one of the prominent sectors where multiple use cases are best fit to drive data analytics. One of the major influential factor which drives shifting of technology for banking entrepreneurs from relation database management systems to Big data warehouse systems is the cost. The other areas where Big data technologies supersedes traditional RDBMS technologies are data reliability, performance, and heterogeneous data structure support. Thus, an essential training on big data technologies such as Hadoop, Spark would help banking enterprises to step ahead in technology dominated business world.
Browse syllabus for Hadoop
Most of the banking enterprises have data stored in varieties of structures for different purposes. It might be possible case that employee information list might have been stored in excel sheet, loan files are maintained in Oracle database, and customer transactional logs are maintained in plain files. For an integrated analytics of over all system, the data must be extracted to a common unified platform where all analytics could be easily done with high performance irrespective of the high volume of data in an affordable cost. The operation should be feasible to carry out using commodity hardwares in their own local cluster with assured higher degree of data reliability and performance.
Realization of solutions to such problem could be done with Big data technologies. Big data training course skills for IT officers in banking enterprises would help to evaluate and access their current data sets and find out the best use case that can be adopted using big data. Our in depth regressively revised Hadoop training course syllabus and research oriented big data expert development and training team would ensure to make you learn what is essentially required to learn as a nectar of vast big data lake.
Thus we foresee, our Big Data skill development training program for Banking Senior IT officers as an invaluable beneficial asset to the enterprise. Big data training program are only relevant training programs for IT officers but equally important to senior management level marketing analyst and CEOs too. Providing Big data training to business analysts and management team would help to effectively shape future business directions.
Scope in Nepal
In Contrast to Nepal, Big Data is completely naive platform for banking enterprises, however, According to Forbes – 87% of banking enterprises think that Big Data will make change to their industry before the end of the decade, and not having a Big Data strategy will cause their companies to fall behind. In this regard, As a first step towards Big Data, we think, Banking IT Officers should get trained on preliminaries of how and where Big Data use cases fits in relevance to data the bank has acquired or in future will acquire.
Banking use cases
Through our long research and analysis on banking domains, We have found following applicable areas where banks can opt for use case implementation in respective areas:
- Fraud Detection: Big Data Analytics could be used to differentiate fraudulent interactions from legitimate business transactions and suggest immediate actions, such as blocking irregular transactions.
- Compliance and Regulatory Requirements: Evaluate and analyze abnormal trading patterns.
- Customer Segmentation: Target Promotion and Marketing can be effectively executed when statistics of customer demographics, online interaction data, and external data such as values of customers home etc. is analyzed and reported correctly.
- Personalized Marketing: Finding out buying habits of customers by analyzing data, eg. social profiles, can be a great tool for credit risk assessment using Big Data.
- Sentiment Analytics: Analytics on monitoring customers voice can be a great tool for maintaining a long term good relationship with clients which in turn becomes major factor for market success. Social medial platforms such as Facebook, Twitter has shown up new avenues for enterprises to connect with their customers. Analytics on the large volume of communication such as likes, dislikes, shares, comments about various products and services on these platform can provide valuable business insights .
- Customer 360: Identify customers profile for understanding product and service engagement helps to detect customers who are about to leave.
Many Banks have used customer life cycle events to boost credit card activation. One the way to target these types of target promotion is by using personalized messages to each of the life cycle segments which analytics team has identified. The result of this would increase significant number of credit card activation and overall cost reduction in customer acquisition.
Having insights on Big Data technology right now, can be a beneficial long term strategic planning for competitive top level banking enterprises.
Course Duration: 90Hrs
• What is Big Data?
• challenges for processing big data?
• Technologies support big data?
• What is Hadoop?
• Why Hadoop?
• Hadoop History
• Use cases of Hadoop
• RDBMS vs Hadoop
• When to use and when not to use Hadoop
• Hadoop Ecosystem
• Vendor comparison
• Hardware Recommendations & Statistics
Significance of HDFS in Hadoop
Features of HDFS
5 daemons of Hadoop
1. Name Node and its functionality
2. Data Node and its functionality
3. Secondary Name Node and its functionality
4. Job Tracker and its functionality
5. Task Tracker and its functionality
Data Storage in HDFS
1. Introduction about Blocks
2. Data replication
• Accessing HDFS
1. CLI (Command Line Interface) and admin commands
2. Java Based Approach
• Fault tolerance
• Download Hadoop
• Installation and set-up of Hadoop
1. Start-up & Shut down process
• HDFS Federation
• Map Reduce history
• Architecture of Map Reduce
• Working mechanism
• Developing Map Reduce
• Map Reduce Programming Model
1. Different phases of Map Reduce Algorithm.
2. Different Data types in Map Reduce.
3. Writing a basic Map Reduce Program.
• Driver Code
• Creating Input and Output Formats in Map Reduce Jobs
1. Text Input Format
2. Key Value Input Format
3. Sequence File Input Format
• Data localization in Map Reduce
• Combiner (Mini Reducer) and Partitioner
• Hadoop I/O
• Distributed cache
• Introduction to Apache Pig
• Map Reduce Vs. Apache Pig
• SQL vs. Apache Pig
• Different data types in Pig
• Modes of Execution in Pig
• Grunt shell
• Loading data
• Exploring Pig
• Latin commands
• Hive introduction
• Hive architecture
• Hive vs RDBMS
• HiveQL and the shell
• Managing tables (external vs managed)
• Data types and schemas
• Partitions and buckets
• Architecture and schema design
• HBase vs. RDBMS
• HMaster and Region Servers
• Column Families and Regions
• Write pipeline
• Read pipeline
• HBase commands