01 - Data lakes, lakehouses, and more (2.74 MB)
02 - What you should know (2.4 MB)
03 - Capstone project preview (4.98 MB)
01 - What is a data lake (7.82 MB)
02 - Origins and evolution (6.42 MB)
03 - Architecture core components (8.01 MB)
04 - Data lake vs data warehouse (5.97 MB)
05 - Data lake vs data mesh (5.53 MB)
01 - Storage types (5.67 MB)
02 - Storage hosting (6.06 MB)
03 - Storage solutions S3, GCS and Azure Blob Storage and HDFS (6.85 MB)
04 - Folder structures (6.91 MB)
05 - File formats (4.96 MB)
06 - Data compression (6.24 MB)
07 - Data partitioning (10.23 MB)
01 - Data ingestion methods (5.85 MB)
02 - ETL vs ELT (4.83 MB)
03 - Data transformation (5.81 MB)
04 - Data quality (8.01 MB)
05 - Error handling, logging, and monitoring (7.72 MB)
06 - Orchestration (10 MB)
07 - Data ingestion platforms (7.39 MB)
01 - Introduction to data management and governance (5.53 MB)
02 - Metadata management (4.49 MB)
03 - Data cataloging (4.89 MB)
04 - Data lineage (2.88 MB)
05 - Data security, privacy, and compliance (5.27 MB)
06 - Data management tools and platforms (7.7 MB)
01 - What is a data lakehouse (8.48 MB)
02 - ACID transactions (6.71 MB)
03 - Schema management (8.22 MB)
04 - Table formats Delta Lake, Apache Iceberg, Apache Hudi (8.27 MB)
01 - Introduction to data consumption (10.28 MB)
02 - Unified data analysis Spark (6.58 MB)
03 - SQL on Hadoop Hive and Impala (5.63 MB)
04 - Interactive query engines Presto and Trino (5.71 MB)
05 - Data indexing (6.57 MB)
06 - Optimizing query performance (10.3 MB)
07 - Data consumption security considerations (8.06 MB)
01 - Unified analytics platforms Databricks and Snowflake (6.58 MB)
02 - Cloud data warehouses BigQuery, Azure Synapse, and Redshift (7.22 MB)
03 - Self-service data platforms Dremio and Starburst (6.2 MB)
04 - Interactive notebooks Jupyter, Zeppelin, Databricks (8.65 MB)
05 - BI tools Tableau, Power BI, Superset, Metabase (6.84 MB)
06 - APIs and services for data consumption (5.9 MB)
01 - Capstone project overview (12.96 MB)
02 - Data model overview (4.34 MB)
03 - Project installation and code walkthrough (12.31 MB)
04 - Infrastructure setup (12.07 MB)
05 - Raw data ingestion (24.78 MB)
06 - Transformation models overview (8.9 MB)
07 - Solution Build a data model with SQL (2.78 MB)
08 - Executing data transformations (8.28 MB)
09 - Data orchestration (8.51 MB)
01 - Dremio walkthrough (5.44 MB)
02 - Executing queries and creating virtual datasets (9.93 MB)
03 - Creating complex virtual datasets using SQL (7.16 MB)
04 - Connecting Dremio to Apache Superset (4.44 MB)
05 - Creating a marketing dashboard (20.15 MB)
06 - Connecting Dremio to Jupyter Notebook (10.9 MB)
07 - Advanced product reviews analytics (23.67 MB)
08 - Solution Vehicle health analytics in Jupyter (9.29 MB)
01 - Introduction to LLMs and vector embeddings Llama (6.63 MB)
02 - Introduction to RAG (retrieval-augmented generation) (2.51 MB)
03 - Introduction to vector databases Chroma (3.49 MB)
04 - What is Langchain (1.79 MB)
05 - Generative AI project overview Sales copilot (7.58 MB)
06 - Installation and code walkthrough (10.92 MB)
07 - Project execution Using the copilot (24.53 MB)
01 - Recap and key takeaways (3.24 MB)
02 - Next steps on your data journey (2.85 MB)