Introduction to Cloud, Big Data, ML, IoT, AI and how they are connected with each other. This is an introduction deck to students

1. The world with Cloud, Big Data, ML,
IoT and AI
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2. How it started?
◦ Big Bang - Document Management
One of the galaxies was Documentum . It was created in June 1990.
They developed a customized system for Boeing to organize, store, maintain, and selectively publish the
thousands of pages of information for the Boeing 777 training manuals.

3. Information Ecosystem
◦ Types of Data
◦ Structured
◦ Unstructured
◦ Enterprise Content Management
◦ Web Content Management
◦ Parallel Computing
◦ Federated Search
◦ Data Lake
Image source - Azure

4. Hadoop
Hadoop ecosystem:
• Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop
MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and
ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
• Avro™: A data serialization system.
• Cassandra™: A scalable multi-master database with no single points of failure.
• Chukwa™: A data collection system for managing large distributed systems.
• HBase™: A scalable, distributed database that supports structured data storage for large tables.
• Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
• Mahout™: A Scalable machine learning and data mining library.
• Pig™: A high-level data-flow language and execution framework for parallel computation.
• Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of
applications, including ETL, machine learning, stream processing, and graph computation.
• Submarine: A unified AI platform which allows engineers and data scientists to run Machine Learning and Deep Learning workload in distributed cluster.
• Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG
of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and
also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.
• ZooKeeper™: A high-performance coordination service for distributed applications.

5. Cloud Computing
◦ Simply put, cloud computing is the delivery of
computing services—including servers, storage,
databases, networking, software, analytics, and
intelligence—over the Internet (“the cloud”)
◦ Ex -Gmail. Gmail users can access files and
applications hosted by Google via the internet
from any device.
Source – Amazon

6. Machine Learning
◦ Derive information from massive amounts of data to answer complex questions
Data Training Model Predictions

7. IoT – Internet of Things
◦ There are billions of electronic devices around the world that are connected to the internet, collecting,
processing and transmitting data
◦ This is possible due to use of multiple technologies like analytics, machine learning, sensors, embedded
systems, and cloud computing
Image source - AWS
Image source - Azure

8. AI
◦ Its an intelligence demonstrated by machines in problem solving by analyzing and processing data
◦ Advanced form of ML
◦ Dated back to 1955.
◦ Based on assumption that machines can be made to think and act like Humans
◦ Suffer crisis called AI Winter
Image source - AWS

9. Thank you!
Contact us for specialized coursed in Cloud and IoT
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