Section 3: Incremental Data Processing

A SQL statement that combines UPDATE and INSERT operations to efficiently load data into a target table, handling both existing and new records.

A SQL statement that inserts data from an external source into a table, often from a CSV or other file format.

Ensuring data integrity and avoiding redundant entries within a table.

A Databricks table format designed for efficient data management and analysis, offering features like ACID properties and time travel.

The process of loading data from an external source into a table.

A cloud storage service offered by Amazon Web Services.

A cloud storage service offered by Microsoft Azure.

A file format typically used for storing tabular data, often with comma-separated values.

Actual information stored, processed, and analyzed in Databricks. This could include tables, files, logs, sensor readings, and transaction records.

Information that describes data itself, providing context and organizational details. Examples in Databricks include schema, data source details, timestamps, authorship, and lineage.

Represents the layout of a table, defining the names and data types of each column. It's an essential part of metadata.

Information about the location and origin of data. Helps track where data comes from, ensuring traceability and accuracy.

A Databricks table where the data is stored and managed within the Databricks platform. Databricks handles the data lifecycle for these tables.

A Databricks table that points to external data stored outside of Databricks, like in a cloud storage service (e.g., S3). Users manage the data's lifecycle.

The record of changes made to data, providing a history of how it evolved over time.

When data is moved or transformed, tracking where it came from and what changes were applied. Helps maintain trust and transparency.

A feature in Delta Lake that allows you to specify multiple columns for Z-ordering, improving data locality and query performance.

In Delta Lake, Z-ordering helps colocate related data based on the specified columns, improving data locality for queries that filter on those columns.

A command in Delta Lake used to clean up old files that are no longer needed, freeing up storage and improving query performance.

When you make changes to a Delta table (like updates or deletes), Delta Lake creates new files and marks the older ones as unused.

By default, Delta Lake keeps old, unused files for a specified retention period (7 days). This allows for time travel and rollback operations.

The VACUUM command permanently removes old files from storage based on the specified retention period. You can control this period.

By using a shorter retention period, you reduce the ability to time travel to older versions of the Delta table.

The VACUUM command is used to clean up old and unused files, significantly improving query performance and reducing the number of files to scan.

The location where processed data is stored in a Databricks pipeline. Often a Delta table but could also be other formats like Parquet or JSON.

A set of pre-written code, functions, and packages you can import into your Databricks notebook to enhance its functionality. This allows you to reuse already-built tools for common tasks in your data pipeline.

In Databricks, this is a code block that defines how to read data from a source, typically a file or a database table.

In DLT, this is a code block that defines how to transform data from a source table into a new table. You can apply various operations like filtering, aggregation or transformations.

A type of data storage designed specifically for data lakes, known for its ACID properties and efficient updates, making it ideal for data pipelines.

The DLT pipeline's schedule which specifies when the pipeline should run, like daily, weekly, or on demand.

Data Lake Table (DLT) is a declarative approach to building data pipelines in Databricks. You define the transformations and the target table, and DLT handles the execution and updates automatically.

Triggered pipelines are ideal for situations where cost efficiency is a priority and flexibility in data latency is acceptable. They are well-suited for batch processing and scheduled data updates.

Continuous pipelines excel when low latency is crucial and real-time data processing is required. They're perfect for real-time analytics, monitoring, and alerting.

Triggered pipelines involve periodic runs, resulting in lower compute costs because resources are used only during execution.

Continuous pipelines run continuously, leading to higher compute costs as resources are always active.

Triggered pipelines have higher latency as data processing depends on the schedule frequency.

Continuous pipelines have lower latency due to real-time data processing, offering faster insights.

Triggered pipelines handle data in batches, processing data collected over a period.

Continuous pipelines process data as it arrives, enabling real-time analysis.

A Spark DataFrame method that provides a summarized description of the streaming data structure, similar to describe() on a static DataFrame.

A Spark DataFrame method that provides a detailed explanation of the streaming query execution plan, often used for troubleshooting and optimization.

A Databricks feature that automatically ingests data from cloud storage into Delta Lake tables, making it ideal for continuous data pipelines.

The designated location where Auto Loader reads data from (e.g., an S3 bucket or Azure Blob storage container).

A scenario where Auto Loader is particularly beneficial, allowing for real-time analysis of constantly generated log files.

The key advantage of Auto Loader for log ingestion: automatically loading new log data as it becomes available.

The use of Auto Loader enhances real-time log analysis by providing a steady flow of fresh data for immediate insights and monitoring.

A scenario where Auto Loader is especially valuable, enabling efficient data ingestion and analysis of large datasets, such as web logs, sensor data, or financial transactions.

The MERGE statement in SQL combines multiple operations (insert, update, delete) into a single transaction for efficient data loading into target tables.

The ON clause in a MERGE statement specifies the condition that must be met for a match between records in the source and target tables.

The WHEN MATCHED clause in a MERGE statement performs actions on records found in both the source and target tables.

The WHEN NOT MATCHED clause in a MERGE statement handles actions on records found only in the source table, inserting them into the target.

The MERGE statement's efficiency comes from combining multiple operations into a single transaction, reducing complexity and improving performance.

The MERGE statement simplifies data management by centralizing update, insert, and delete actions, making it easier to keep data consistent.

The MERGE statement's key to efficiency is the combination of operations (insert, update, delete) into a single atomic transaction, improving performance and reducing complexity.

The MERGE statement simplifies data management by consolidating update, insert, and delete actions, making data synchronization easier, especially for incremental updates.

A command that combines small files in a Delta Lake table into larger files, improving query performance and reducing metadata overhead. It primarily focuses on compacting data files stored in Parquet format.

A feature in Databricks that automatically ingests data from cloud storage into Delta Lake tables, enabling efficient continuous data pipelines. It's useful for real-time log ingestion and analysis.

The process of ensuring that the time frame within which deleted data can be recovered matches the requirements set by the data recovery and auditing processes.

A SQL statement that combines UPDATE and INSERT operations to efficiently load data into a target table, handling both existing and new records. It helps ensure data integrity by updating existing records or inserting new ones based on a specified condition.

The COPY INTO statement in Databricks is a powerful tool for quickly and efficiently loading data from external sources into Delta tables. It is particularly useful for bulk data loading operations from storage services like Amazon S3 or Azure Blob Storage.

Data deduplication is the process of eliminating duplicate records from a dataset to ensure data integrity and avoid redundancy. It's essential for maintaining accurate and reliable data.

Delta tables in Databricks are a foundational data format with ACID properties (Atomicity, Consistency, Isolation, Durability). These traits guarantee data integrity and reliable transaction handling. Delta tables support versioning for time travel, allowing you to recover lost data or understand past states. They are highly efficient at performing updates and deletes.

In a Databricks pipeline, the "target" is where loaded data is stored after processing. This is typically a Delta table, but it could also be other formats like Parquet or JSON files.

The COPY INTO statement is a powerful tool for loading data from external sources into Delta lakes, especially in situations where you need to deal with large datasets. It helps ensure that data is loaded efficiently and without duplicates.

A process designed to ensure that data loaded into a table is unique and avoids duplicates. This can involve checking for existing records, using keys, or pre-processing data to remove duplicates.

Ensuring that a statement or process, if performed multiple times, yields the same result as if done only once. It helps prevent unintended duplicates.

A feature in Delta Lake that ensures only relevant data is loaded during a COPY INTO operation, enhancing efficiency and optimizing data ingestion.

A method for identifying and handling existing records during data ingestion. This ensures that new records are properly inserted without creating duplicates.

A pre-processing step to eliminate duplicate records before ingesting data into a table. It ensures that the source data itself doesn't contain duplicates.

A built-in feature of Delta Lake that can be used effectively to clean up unused files (from old versions) and improve the efficiency of querying data.

Features present in Delta Lake that improve the efficiency of loading data by streamlining the process and focusing only on relevant data.

A unique constraint prevents duplicate entries in a table, ensuring data integrity and preventing accidental duplicate insertions.

Change Data Capture (CDC) is a technique that tracks alterations in a database over time. It captures insertions, updates, and deletions in a source table and replicates those changes to a target table or system, keeping data synchronized across systems.

This technique involves a set of rules used to handle constraint violations during database operations like updates or inserts. It helps decide how the database should respond to such violations.

It completely discards any row that violates a constraint, leading to data loss. This can help ensure data integrity by removing inconsistent or invalid data.

This rule causes the entire update operation to fail if any constraint violation occurs. No changes are made to the database when a violation is detected.

It's a dedicated tool in Databricks for managing data pipelines, which uses a declarative approach.

This feature in Databricks allows automated ingestion of data from cloud storage into Delta Lake tables. It's ideal for continuous data pipelines due to its automatic nature.

It's a scheduling mechanism in DLT pipelines that executes a series of data transformations at specified intervals. It's used for batch processing and scheduled updates.

It's a continuous data processing approach in DLT where transformations are applied in real time as data arrives. It's suitable for real-time analytics and monitoring.

These libraries are pre-written code that can be used in Databricks notebooks to perform common tasks, such as data cleaning or transformation. They encourage modularity and code reuse.

Triggered pipelines run on a schedule, processing data in batches at predefined intervals. They are cost-effective, but they can be slower due to their batch-oriented approach.

Continuous pipelines process data continuously as it arrives, ensuring near real-time analysis. They offer low latency but can be more expensive because they run constantly.

Triggered pipelines have a higher latency because they process data in batches at scheduled intervals.

Continuous pipelines offer low latency because they process data as it arrives, enabling real-time analysis and insights.

A Delta Lake table is a table format specifically designed for data lakes, known for its efficient updates, ACID properties, and time travel capabilities. It's commonly used in Databricks for managing data pipelines.

The transaction log in Delta Lake records every change or modification made to a table. It's essentially a history book of all operations, ensuring data consistency and allowing time travel.

In Databricks, a managed table is stored and managed completely within the Databricks platform, while an external table points to data stored outside of Databricks, often in cloud storage.

The DESCRIBE DETAIL command in Databricks is used to get detailed information about a table, including its location, size, and creation time, making it helpful for understanding how data is stored.

Z-ordering is a technique in Delta Lake for improving query performance by arranging data based on specific columns, ensuring related data is stored close together. This improves data locality and reduces the amount of data that needs to be scanned.

The VACUUM command in Delta Lake is used to clean up old and unused files from a table, freeing up storage space and improving query performance. This optimization removes old versions of files that are no longer needed.

The COPY INTO statement in Databricks is a powerful and efficient way to load data from external sources into a table, often from a CSV or other file format, simplifying the data ingestion process.

A MERGE statement is a SQL statement that combines UPDATE and INSERT operations into a single transaction. It allows efficient loading of data into a target table, handling both existing and new records based on a specified condition, ensuring data consistency.

The RESTORE TABLE command in Databricks lets you recover a Delta table to a previous version by specifying the version or timestamp you want to roll back to.

The DESCRIBE HISTORY command provides a detailed history of a Delta table, including all versions and timestamps. This helps identify which version you want to roll back to.

The RESTORE TABLE command is used to roll back a Delta table to a specific version. You can choose either a version number or a specific timestamp.

The TO VERSION AS OF clause in the RESTORE TABLE command specifies the desired version to roll back to.

The TO TIMESTAMP AS OF clause in the RESTORE TABLE command specifies a specific timestamp to roll back to.

Delta tables efficiently store data changes in a transaction log, allowing you to easily retrieve past versions or states of the table. This feature is called time travel.

Rolling back a Delta table using the RESTORE command can discard changes made after the selected version or timestamp. It's crucial to ensure that you're not losing important data before performing a rollback.

It's always a good practice to create a backup of the current state of a Delta table before performing any rollback operation. This provides a safety net in case you need to revert to the current state.

A SQL statement that creates a new table or replaces an existing one with a new definition. It allows for defining the schema, storage format, and other properties of the table. If the table already exists, it replaces the entire table, including its schema and contents.

A SQL statement used to overwrite the existing data in a table with new data. It replaces the entire content of the table with the provided values.

A comment added to a table definition. It provides a description or explanation of the table's purpose or function.

A storage format for structured data in Databricks, known for its ACID properties (Atomicity, Consistency, Isolation, Durability). It enables reliable data management and supports time travel for recovering past data.

A SQL command used to add a comment to a table definition. It allows for detailed explanations or metadata about the table.

A statement used in Databricks to specify the storage format for tables. It defines how data is stored and managed, allowing for optimized performance, data integrity, and access to past data.

In Databricks, INSERT OVERWRITE TABLE replaces the existing content of the table with the new data provided. CREATE OR REPLACE TABLE creates a completely new table, dropping the existing table and its entire contents.

A set of rules in SQL that define how the database should handle constraint violations during operations like updates or inserts. It determines how to respond to inconsistent or invalid data.

A SQL statement used in Databricks to create new Delta tables. It defines the table name and the columns it will contain. This statement allows you to create a structured table with specific data types for each column.

A SQL statement in Databricks for efficiently loading large amounts of data into a Delta table. It specifies the source data location, file format, and any necessary options.

A framework in Databricks that simplifies the building of data pipelines. DLT allows you to declaratively define your pipeline logic, automating the process of loading, processing, and updating data.

A Databricks feature that optimizes the performance of queries by combining small files in a Delta Lake table into larger, more compact files. This reduces metadata overhead and improves query speed.

Triggered pipelines execute data transformations at scheduled intervals, making them cost-effective for batch processing and scheduled updates.

Continuous pipelines process data in real-time as it arrives, enabling near real-time analysis and monitoring.

Auto Loader automatically loads data from cloud storage locations into Delta Lake tables, enabling continuous data pipelines.

Cost-effective approach for batch processing and scheduled updates, but with higher latency due to scheduled intervals.

Offers low latency for near real-time analysis, but can be more expensive as it continuously processes data.

The process of identifying and handling existing records during data ingestion to prevent duplicates. Ensures new records are safely inserted without overwriting existing ones.

A constraint that ensures values in a column meet a specific condition. If violated, the operation fails, and the transaction is rolled back.

Ensures columns do not contain NULL values. Violations result in errors.

Ensures each record has a unique identifier. Violations result in errors.

Ensures unique values in specified columns. Violations result in errors.

A clause used to specify the behavior when a constraint violation occurs during data operations. It can be used for removing rows or failing the entire operation.

Automatically drops the row that violates the constraint, resulting in partial data loss. Useful for data cleansing or filtering tasks.

Causes the update operation to fail if any constraint violation occurs. No changes are made to the database. Ensures data integrity.