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Introduction to Big Data and Machine Learning Dr. Mihail November 12, 2019 (Dr. Mihail) Intro Big Data November 12, 2019 1 / 17 Big Data - What is it? Data Data can be defined as information in raw or unorganized form a . Broadly defined,

  1. Introduction to Big Data and Machine Learning Dr. Mihail November 12, 2019 (Dr. Mihail) Intro Big Data November 12, 2019 1 / 17

  2. Big Data - What is it? Data Data can be defined as information in raw or unorganized form a . Broadly defined, the “big” in Big Data” refers to datasets that cannot fit in the resources of a single machine, maybe even a supercomputer. a http://www.businessdictionary.com/definition/data.html Big Data Analytics The process of analyzing Big Data to provide past, current and future statistics and any other insights useful to decision making. Big Data analytics typically can be categorized in: Data Analytics: deals with collection and interpretations, focus on past Data Science: deals with predictive and prescriptive analytics, focus on present and future (Dr. Mihail) Intro Big Data November 12, 2019 2 / 17

  3. Analytics vs. Science (Dr. Mihail) Intro Big Data November 12, 2019 3 / 17

  4. Conventional Data Analytics Schema Relational Database Management Systems (RDBMS) Data warehouses and Data marts for analytics used Schema-on-Write approach Traditional data warehouses designed for Extract, Transform and Load (ETL) tasks Predefined questions are then answered using SQL queries ETL pipelines developed to load data into the database in a consumable format (Dr. Mihail) Intro Big Data November 12, 2019 4 / 17

  5. Big Data Analytics New Era of Data No longer use schemas. Why? Too much initial work (one to six months) Any change requires developers and forces predefined boundaries Processing structured and unstructured data is challenging in traditional RDBMs (e.g., large binary images or videos) Instead use Schema-on-Read (SOR) (Dr. Mihail) Intro Big Data November 12, 2019 5 / 17

  6. Schema-on-Read vs. Schema-on-Write (Dr. Mihail) Intro Big Data November 12, 2019 6 / 17

  7. Big Data Project Lifecycle Lifecycle (Dr. Mihail) Intro Big Data November 12, 2019 7 / 17

  8. Hadoop and Spark Flexibility Large-scale data pre-processing Exploration of extremely large sets of data Accelerating data-driven innovation by providing schema-on-read approach Variety of tools and APIs for data exploration (Dr. Mihail) Intro Big Data November 12, 2019 8 / 17

  9. Data Scientists vs. Software Engineers Differences Software engineers develop general-purpose software for applications based on business requirements Data scientists don’t develop application software, but they develop software to help them solve problems Typically, software engineers use Java, C++, and C-sharp programming languages Data scientists tend to focus more on scripting languages such as Python and R (Dr. Mihail) Intro Big Data November 12, 2019 9 / 17

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