Data Lake Fundamentals

Explore how data lakes differ from data warehouses, relational and NoSQL databases, data marts, and data hubs. Learn about schema on read versus schema on write and analytics use cases.

Explore real time use cases of data lakes across industries, enabling streaming analytics for customer insights, fraud detection, IoT predictive maintenance, real time supply chain monitoring, and cybersecurity insights.

Evaluate your data architecture to align with your business objectives, use cases, data strategy, and roadmap. Define data sources, governance, security, and deployment options for a scalable data lake.

Data lakes offer versatility by accepting all data types without heavy structuring and centralize data for easy access, scaling with growing data volumes and reducing costs through on-demand processing.

Navigate data lake challenges by enforcing governance, quality checks, and metadata management to ensure data accuracy, security controls, documentation, amid diverse raw data and real time data ingestion.

Define data lake objectives and use cases; involve stakeholders; establish governance with ownership, access controls, metadata management; adopt schema on read, catalog, quality checks; ensure security, scalability, and cost efficiency.

Explore key data lake technologies, including HDFs, Apache Spark, Amazon S3, Azure Data Lake Storage, and Google Cloud Storage, and learn how they power scalable, secure data lake architectures.

Explore leading data lake vendors, including Cloudera’s open platform based on Hadoop and Apache Spark, Databricks, Snowflake, AWS Lake Formation, and Azure Synapse Analytics.

Explore open-source data lake solutions for scalable storage and processing, including Hadoop, Spark, Flink, Prestodb, Apache Arrow, and Delta Lake for reliability and fast queries.

Explore lambda architecture in data lakes, applying patterns and guidelines to support both historical or batch and real-time data, with distinct layers that enforce separation of concerns across domains.

Explore the principles of lambda architecture, including fault tolerance, immutable data, and recomputation, and learn how a data lake stores raw, immutable data to enable recomputation and prevent data loss.

Explore the lambda architecture with its batch, speed, and serving layers; batch views replace raw data, speed layer enables near real-time updates, and serving layer merges results for final queries.

Learn to build a simple data lake with Python and SQLite, collecting, storing, retrieving, analyzing (mean and standard deviation), and visualizing timestamped data using pandas and matplotlib.

Implement a data lake project using Python with SQLite3, pandas, and matplotlib to store, retrieve, analyze mean and standard deviation, and visualize timestamped data.

Explore how a data lake serves as a centralized repository for structured and unstructured data, with raw, refined, and curated layers and simple transformations like reversing a first name.

Implement a data lake in Python by ingesting csv and json datasets into raw, refined, and curated layers. Add a processed flag and reverse first names in curated data.

Bridge academia and industry with high quality, industry-aligned training, forging partnerships with educational institutions and tech giants to empower lifelong learning that evolves with the digital landscape.