Data Lake Fundamentals: A Quick Intro Guide

Explore how to set up Python in Jupyter through Anaconda, then use Pandas and PyArrow to convert and verify CSV and Parquet files, illustrating a modern data lake workflow.

Install the Anaconda distribution to use Python, then open Jupyter Notebook from the Anaconda prompt and organize projects in a dedicated folder.

Discover core Jupyter notebook features, including renaming and backups, auto-save and checkpoints, editing modes, running code cells, markdown text, and powerful keyboard shortcuts for cell management and kernel control.

Design a simple data lake with python in a jupyter notebook, ingest raw csv and json data, and convert to parquet for faster analytics and smaller files.

Understand why teams store data in a data lake and apply structure only at query time, enabling fast-changing sources, future use cases, and efficient analytics with columnar formats.

Create a simple data lake in a Jupyter notebook using Python, install pandas and pyarrow, and save a two-row csv into the data lake folder.

Data lake principles emphasize storing diverse data securely and accessibly, enabling scalable, flexible, and cost-effective analysis for a broad range of users through governance and quality controls.

Data lake layers bronze, silver, and gold organize data with alternative names such as landing zone, staging area, cleansed, process, curated, integration, graded, business, analytical, reporting, and consumption layers.

Explore the bronze-silver-gold data lake model, where raw data lands in bronze, gets cleaned and transformed in silver, and becomes high-value, KPI-driven gold for decision making.

Understand data lake architecture as a system that stores, manages, and processes data within a lake house. Discover resource management, access connectors, analytics, data classification, ETL, security, and governance.

Explore how a data lake serves as a storage pool. See how data sources, ingestion tools, storage, metadata, processing engines, security and governance, and analytics and visualization tools work together.

Explore data lake fundamentals, from bronze/raw to gold layers, with ingestion tools and PySpark, plus governance, ETL, and data catalog for democratized analytics.

Understand how relational databases shaped structured data management with SQL, explore data silos and their impact on analysis, decision making, and costs, and preview integrated solutions.

Data warehouses integrate data from multiple sources to support business intelligence and analytics, using ETL to extract, transform, and load data. They face limitations with unstructured data and scalability.

Apache Hadoop launched the big data era by storing and processing large unstructured and structured data with an open source framework, using the Hadoop distributed file system and MapReduce.

Explore how data lakes extend Hadoop with cloud storage, databases, and analytics tools. Understand metadata management, data cataloging, governance, and ELT for streaming and analytics.

Accelerate data processing with in-memory speed using Apache Spark, enabling batch and streaming, machine learning, and graph processing within data lakes, complementing Hadoop with APIs in Scala, Python, and Java.

Explore how data lakes built on Hadoop evolved through cloud storage, governance, and advanced analytics to data lakehouses that unify raw data with structured querying and BI-ready analytics.

Explore how data lakes enable growth and innovation across industries, including streaming media, finance, healthcare, omnichannel retail, IoT, digital supply chains, and sales use cases.