Big Data for Managers

Explore the 3 Vs of big data: volume, velocity, and variety, and how they shape data sizes, processing needs, and diverse data types in modern organizations.

Compare traditional relational database technology with big data approaches, highlighting distributed data and processing, data locality, fault tolerance, and the shift to horizontal scalability and cost reduction.

Explain how big data technology uses a cluster of commodity machines to distribute data and processing in parallel, with data local processing and replication for fault tolerance.

Explore Apache Hadoop, focusing on HDFS and MapReduce as the storage and processing backbone of distributed clusters, with HDFS blocks, replication, and MapReduce distribution across nodes.

Explore differentiating No-SQL from relational databases, evaluate big data storage tools like HDFS, HBase, Cassandra, MongoDB, and Impala, and size your cluster for scalable data needs.

HDFS is the Hadoop distributed file system for big data storage, storing blocks with replication on commodity machines, managed by a name node, data nodes, and a secondary name node.

MongoDB is an opensource document oriented No-SQL database storing data as JSON documents with embedded objects and supports horizontal scaling; Impala provides scalable real-time SQL on Hadoop, but is read-only.

Identify internal and external data sources for ingestion, including relational databases, logs, and social media; differentiate batch and streaming data with tools like Flume and Kafka, respecting privacy.

Explore how Flume ingests streaming data into HDFS for big data management, using a source-sink architecture with channels to collect Twitter and other unstructured data.

Master distributed big data analytics from terabytes to petabytes, using data transformation, validation, and mapping between words and numbers for predictive and prescriptive insights.

Map reduce forms the backbone of distributed processing by filtering and grouping data, while Pig and Hive provide data flow and SQL-like capabilities for big data analytics.

Spark enables applying machine learning to business data, including customer segmentation by buying behavior and product segmentation by profit margins, plus predicting sales, detecting security frauds, and forecasting loan defaults.

Storm processes streaming data in real time, parsing logs and saving to HDFS as Hive tables with spouts and bolts, enabling alerts and parallel processing from Kafka messages.

Select a big data tool such as Hadoop, Pig, or Hive for transformation needs. Use Spark for classification, regression, or clustering; rely on Storm for real-time streaming data.

Explore big data visualization with Tableau, QlikView, and Apache Zeppelin, connecting to Hadoop, Hive, HBase, Cassandra, and MongoDB to create live-updating dashboards, heat maps, word clouds, and symbol maps.

Explore Kerberos, a robust MIT authentication protocol issuing tickets for users and resources to secure big data connections, and Apache Knox, which provides a http-proxy gateway for secure Hadoop services.

Learn best practices for big data security by organizing users into groups, assigning read, write, and execute permissions per tool, and leveraging Ranger and Knox to enforce group-based access.

Uncover value from big data by turning insights into actions and leveraging the huge data volume to improve insights. Embrace prescriptive actions powered by the power of intuition.

A warm hello and welcome to lesson 8 of the Big Data for Managers course.

Trace ChatGPT's evolution from GPT 1 to GPT 4, detailing transformers, counts, and next-word prediction. Highlight reinforcement learning from human feedback, biases, fake-news concerns, and data cutoff around September 2021.

Explore how ChatGPT plugins and code interpreter secure data analysis for managers, with data isolation, session-based retention, encrypted transmission, privacy policies against uploading PII, sanitized data and SoC two compliance.

Explore chatGPT demos that show how to explain code, interact like a human, perform prescriptive data analysis, run code and visualize without a plugin, and analyze Kaggle data, with feedback.

Demonstrates ChatGPT with GPT-3.5, explains and rewrites code (binary search, divide and conquer) and provides manager-focused project checklists and big data tool architecture from Oracle to Tableau.

Explore the role of big data in the generative AI revolution, review tools like Midjourney and Pictory, and practice prompts and data analysis and visualization with ChatGPT plus.

Lead successful big data projects by applying the 3 Vs (volume, velocity, variety) through data ingestion, storage, analytics, visualization, and security, using Sqoop, Flume, and the 5 P's model.