Databricks : Delta Live Table (DLT)

Explore data lakehouse architecture, compare data warehouse and data lake, and learn how Delta Lake enables acid transactions, data versioning, governance, and support for analytics, BI, and ML.

Explore Delta Lake's reliability for data lakes with ACID transactions, scalable metadata, and unified streaming and batch processing, plus its storage, engine, sharing, live tables, and connectors.

Create internal and external delta tables in Databricks using sql, define the employee table schema, view catalog history and delta logs, and compare managed versus external storage.

Ingest delta tables using multiple methods, including insert into, insert overwrite, copy into, and data frame writes, with partitioning and table verification.

Perform update, delete, and merge operations on delta tables using sample employee data, updating and deleting records, and applying merge rules to sync source and target tables.

Explore schema enforcement and evolution in delta live tables, showing how parquet ignores schema changes while delta enforces schema and supports merge schema to accommodate evolving data.

Explore how Delta table generated columns auto generate the date of joining from joining_date, and add a year of joining as bigint.

Explore Delta Live Tables, a declarative ETL framework that automates data transformation, monitoring, data quality, and error handling, enabling you to focus on writing SQL and Python pipelines.

Explore how a Databricks Delta Live Table pipeline processes data end-to-end—from cluster setup and data load to quality checks, schema validation, and handling multiple CSV files.

Explore how to define and enforce correct data types in delta live tables using spark schema definitions, casting, and infer schema to ensure integers and date columns.

discover how delta live tables handle schema evolution by merging csv files with different columns using merge schema, resulting in a unified schema with nulls for missing fields.

Learn to build a DLT pipeline using JSON by ingesting simple and complex JSON files, exploring seven attributes and an orders array, and inspecting the schema with data frame.

Learn to flatten json data in delta live table (dlt) by exploding arrays into rows, deriving order id, product, and quantity, and applying this approach in notebooks and dlt pipelines.

Learn how DLT streaming pipelines load CSV data via read stream from cloud files, appending new records to the target table while batch reloads replace data.

Discover how delta live table pipelines use the rescued data column in the target table to capture invalid records instead of dropping them, and how schema changes retrigger the pipeline.

Demonstrates streaming schema evolution in delta live tables by handling a new column, profession two, with merge schema true and automatic re-execution of the pipeline when a schema change occurs.

Learn to apply delta live tables data quality checks with the expectation decorator, using actions like expect, drop, and fail to manage invalid records, enabled in the advanced product edition.

Apply multiple quality checks in Delta Live Table pipelines using expect and fail decorators, plus expect_all decorators, to enforce age > 45 and non-null last names.

Learn how to diagnose and fix Azure quota exceeded exceptions in Delta Live Table pipelines by increasing regional CPU quotas and retriggering the pipeline to achieve success.

Learn to build a DLT pipeline using SQL to process CSV, JSON, and parquet data from a batch volume, creating or refreshing live tables in the Unity Catalog schema.

Derive new tables from a parent dataset using the live keyword and like keyword, creating gender-specific csv, json, and parquet materialized views in a DLT pipeline.

Create a streaming sql pipeline with Databricks dlt to ingest csv, json, and parquet files from a volume, and incrementally append new records to the target table.

Derive new tables from streaming data using Delta Live Table's stream method, filtering where age greater than 45 on csv, json, and parquet sources, then run the DLT pipeline.

Explore how delta live table (dlt) handles schema evolution by merging different schemas across csv, json, and parquet data, and observe automatic pipeline regeneration.

Apply quality checks in Delta Live Table using SQL constraints and expect condition to control invalid records, with options to route to a matrix, drop them, or fail on violation.

Create delta live tables to process json and parquet data in DBFS, building raw tables like customers_json_raw and customer_paket_raw, materialized views, and sample data verification.

Explore a delta live table-based project architecture for a retail data lakehouse, ingesting offline and online data, currencies and ip mappings, forming bronze, silver, and gold layers with dashboards.

Explore medallion lakehouse architecture with bronze, silver, and gold layers forming a multi layered single source of truth for enterprise data products, enabling analytics and machine learning.

Build an ingestion pipeline from sql server to the landing zone, using a watermark to fetch new records, update the control table, and verify data in dbfs, with PyMySQL integration.

build a customer ingestion pipeline from sql server to dbfs using spark jdbc, filtering by last modified time and updating the control table for incremental delta live table ingestion.

Read orders with jdbc, filter updated_at greater than the watermark, write to landing zone and orders folder, update the max updated_at in the control table, and verify in catalog.

Configure Event Hub and create producer to ingest real-time data, send sample json messages, and build a streaming pipeline to load data into the data ingestion folder.

Build a streaming ingestion layer in Databricks by reading data from an event hub, extracting JSON fields, and writing parquet files to the online event landing zone with checkpointing.

Build the bronze layer of the data lake house with a Delta Live Tables pipeline that ingests parquet data from the landing zone into bronze tables.

Builds the silver layer for the products table in a Delta Live Tables pipeline, filters invalid records, adds an updated timestamp column, and deploys the silver table.

Build the silver customer pipeline with Delta Live Tables, encrypt PII data, lowercase emails, normalize country values, and mask sensitive fields using UDFs and sha256.

Build the silver orders pipeline by converting currencies to USD with a UDF, joining with a currency view, and exposing sale_price_usd in Delta Live Tables.

Build a silver online event pipeline in Databricks Delta Live Tables by masking PII, converting sale prices to USD, normalizing country data, and materializing a silver table and its view.

Build the silver web logs pipeline by enriching bronze web logs with geolocation data, converting IPs to numbers, and joining to produce a country-enhanced silver dataset in Delta Live Tables.

Build the gold layer by constructing KPI tables from silver data, including unique customers, country totals, website hits, and yearly sales, then deploy a delta live table pipeline.

Create and run sql queries in sql editor to produce kpi metrics, including country customer counts from gold.country_customers, website hits, and unique customers, saved to the gold layer for dashboards.

Create a lake house dashboard in the Databricks SQL panel by adding visualizations—unique customers (counter), country website hits (bar chart), and yearly product units sold (area chart)—with titles and layout.

Create mock data for a DLT pipeline with a data generate notebook, defining device data source and device data summary, using random, timestamped test rows and validating catalog tables.