Pipelines with Databrick Delta Live Tables Part 1/2

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Questions and Answers

What does the bronze level in Medallion Architecture represent?

Materialized views of cleaned data

Business-level aggregated data

Raw ingestion and history (correct)

Filtered, cleaned, and augmented data

Which statement correctly describes Live Tables in Delta Live Tables?

They are materialized views updated by a pipeline. (correct)

They do not manage data dependencies.

They provide real-time data processing only.

They are designed for batch-only processing.

What is a key feature of Streaming Live Tables?

They process old data multiple times.

They are always built without a data source.

They do not support stateful computations.

They guarantee exactly-once processing of input rows. (correct)

What role does Delta Live Tables play in data quality?

It has built-in declarative quality control to define expectations. Signup and view all the answers

Which of the following best characterizes the silver level in Medallion Architecture?

Filtered, cleaned, and augmented data. Signup and view all the answers

What is the main purpose of Delta Live Tables?

To make ETL easy on Delta Lake. Signup and view all the answers

Which benefit does Delta Live Tables offer concerning latency?

Reduce latency by avoiding reprocessing of old data. Signup and view all the answers

What is required to create a Live Table in Delta Live Tables?

A structured query language syntax is necessary. Signup and view all the answers

What does the gold level in Medallion Architecture involve?

Aggregated business-level data. Signup and view all the answers

Which streaming sources are compatible with Streaming Live Tables?

Appended-only streams like Kafka and Kinesis. Signup and view all the answers

What is the first step in creating a live table pipeline?

Create the Live Table in a notebook Signup and view all the answers

What distinguishes development mode from production mode?

Development mode reuses long-running clusters for quick iteration Signup and view all the answers

How does DLT manage dependencies in a Live Table pipeline?

By detecting Live dependencies and executing operations in the correct order Signup and view all the answers

What is the purpose of using Expectations in data quality management?

To create constraints that validate data correctness during processing Signup and view all the answers

What does the command 'Select * from cloud_files(files);' accomplish?

It uses the auto-loader to reference cloud files Signup and view all the answers

What is a key characteristic of production mode regarding cluster management?

Clusters are shut down as soon as they are done processing tasks Signup and view all the answers

How can another table created in a different notebook be referenced?

Through the Live virtual schema Signup and view all the answers

What happens when an expectation on data quality fails?

The rows violating the expectation are dropped Signup and view all the answers

What does DLT do to ensure data lineage capturing?

It automatically detects and manages Live dependencies Signup and view all the answers

What type of SQL command is 'EXPECT (timestamp_col > 'timestamp value') ON VIOLATION DROP;' classified as?

Data quality constraint Signup and view all the answers

What is the default behavior of DLT when handling bad records?

Track the number of bad records Signup and view all the answers

Which feature allows the visualization of data flows between tables in a pipeline?

Pipelines UI Signup and view all the answers

Which of the following is NOT a function of the event log?

Control pipeline permissions Signup and view all the answers

Which requirement must be met for a streaming table using the SQL stream() function?

It must be an append-only table. Signup and view all the answers

What does the command 'Create Streaming Live table my_stream as Select * from STREAM(table_name);' do?

Creates a live table to continuously accept new records. Signup and view all the answers

What is a limitation for streaming tables regarding the APPLY CHANGES INTO command?

They cannot be the target of APPLY CHANGES INTO. Signup and view all the answers

What is one purpose of configuration parameters in DLT?

To modularize code by creating variables. Signup and view all the answers

When targeting schemas in pipelines, what term is used to refer to the active schema?

Live schema Signup and view all the answers

Which of the following is NOT a type of record that DLT tracks?

Deleted records Signup and view all the answers

In a streaming context, what must be true regarding the data being read?

It must be read from an append-only delta table. Signup and view all the answers

Study Notes

Medallion Architecture

Defines three levels for data pipelines: bronze, silver, and gold.
Bronze represents raw data ingestion and history.
Silver involves filtering, cleaning, and augmenting data.
Gold represents business-level data, including aggregates.

Delta Live Tables (DLT)

Focus on simplifying ETL processes on Delta Lake.
Provide declarative tools to build batch and streaming data pipelines.
Offer built-in declarative quality control, allowing users to declare data quality expectations and actions to take when violations occur.
Enable easy scaling of infrastructure alongside data.

Live Tables

Materialized views for the lakehouse.
Defined by SQL queries.
Created and kept up-to-date by pipelines.
Provide tools for managing dependencies, controlling quality, automating operations, simplifying collaboration, saving costs, and reducing latency.

Creating Live Tables

Use SQL syntax: CREATE LIVE TABLE table_name AS SELECT col_name FROM another_table;

Streaming Live Tables (SLT)

Based on Spark Structured Streaming.
Ensure exactly-once processing of input rows.
Inputs are only read once.
Compute results over appended-only streams, such as Kafka, Kinesis, or Auto Loader.
Allow for cost and latency reduction by avoiding reprocessing of old data.
Created with SQL: CREATE STREAMING LIVE TABLE table_name SELECT * FROM cloud_files(files); (using Auto Loader)

Live Table Pipeline

Requires three steps:
- Create the Live Table in a notebook.
- Create the pipeline with one or more notebooks.
- Run the pipeline.

Development vs Production Mode

Development Mode: reuses long-running clusters for faster iteration, no retries on errors enabling faster debugging.
Production Mode: cuts cost by turning off clusters after completion (within 5 minutes), escalating retries including cluster restarts ensures reliability in the face of transient issues.

Dependent Tables and Live Virtual Schema (LVS)

Dependencies owned by other products are read from the catalog or Spark data sources as normal.
Live dependencies from the same pipeline are read from the Live schema.
DLT detects Live dependencies and executes all operations in the correct order.
DLT handles parallelism and captures the lineage of the data.
Live dependencies are referenced using SQL: CREATE LIVE TABLE table_name AS SELECT * FROM Live.live_table_name;

Data Quality with Expectations

Expectations serve as tests ensuring data quality in production.
Implemented using SQL constraints or Python functions.
Examples:
- SQL: CONSTRAINT valid_timestamp EXPECT (timestamp_col > 'timestamp value') ON VIOLATION DROP;
- Python: @dlt.expect_or_drop("valid_timestamp", col("timestamp_col") > 'timestamp value')
Can be configured to track bad records, drop bad records, or abort processing for a single bad record.

Pipelines UI

Allows visualizing all pipeline information.
Features include:
- Visualization of data flows between tables.
- Discovery of metadata and quality of tables.
- Access to historical updates.
- Control operations: switching between dev and prod modes, deleting the pipeline, managing user permissions, scheduling, etc.

Event Log

Automatically records all pipeline operations.
Records operational statistics, including time and current status of operations, pipeline cluster configurations, and row count.
Captures provenance, including table schemas, definitions, declared properties, table-level lineage, and query plans used to update tables.
Tracks data quality through expectations, including pass/fail statistics and input/output rows that caused expectation failures.

SQL Stream Function

stream() function allows streaming data from any Delta table.
Reads a stream of new records instead of a snapshot.
Applicable only to append-only tables.
Example: CREATE STREAMING LIVE TABLE my_stream AS SELECT * FROM STREAM(table_name);

Restrictions for Streaming Tables

Cannot be the target of APPLY CHANGES INTO (CDC).
Cannot define an aggregate function..
Cannot be a table on which DML operations (delete/update) have been executed.

Configuration Parameters

Enable modularization by creating variables that can be used in any notebook.
Stored as key-value pairs.
Example: "my_vars.etl_path": "s3://path/json/"
Accessed in SQL: CREATE STREAMING LIVE TABLE table_name SELECT * FROM cloud_files("${my_vars.etl_path}", "json")
Accessed in Python: input_path = spark.conf.get("my_vars.etl_path") spark.readStream.format("cloud_files").load(input_path)

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Description

This quiz covers the Medallion Architecture framework, highlighting the three levels of data processing: bronze, silver, and gold. It also explores Delta Live Tables (DLT), which simplify ETL processes in Delta Lake and enable users to build resilient data pipelines using SQL. Test your knowledge on these essential concepts in data engineering!