Source URL: https://cloud.google.com/blog/products/data-analytics/automate-sql-translation-databricks-to-bigquery-with-gemini/
Source: Cloud Blog
Title: Intelligent code conversion: Databricks Spark SQL to BigQuery SQL via Gemini
Feedly Summary: As data platforms evolve and businesses diversify their cloud ecosystems, the need to migrate SQL workloads between engines is becoming increasingly common. Recently, I had the opportunity to work on translating a set of Databricks SQL queries to BigQuery SQL — a task that is deceptively complex due to differences in syntax, functions, and execution behavior.
To streamline the process, we turned to Google Gemini, a powerful AI assistant, to help bridge the gap between the two SQL dialects. In this blog post, I’ll walk you through the process, challenges we faced, how Gemini helped, and key takeaways from the experience.
The translation tightrope: Why it’s tricky
To boost operational efficiency and cut costs, we migrate analytics workloads from Databricks SQL (on Delta Lake tables) to Google BigQuery. This required rewriting numerous queries, from simple aggregations to intricate CTEs and window functions.
Databricks, with its powerful Spark SQL capabilities, and BigQuery, a serverless and highly scalable data warehouse, are both titans in the data world. However, their SQL dialects, while sharing common ANSI SQL foundations, have distinct variations. Translating between the two manually was possible, but would have been time-consuming and error-prone. This is where Google Gemini played a crucial role.
Below are some of the data type mappings between Databricks and Bigquery :
Category
Databricks SQL Data Type
BigQuery SQL Data Type
Description
Integer Types
TINYINT
INT64
8-bit integer
SMALLINT
INT64
16-bit integer
INT or INTEGER
INT64
32-bit integer
BIGINT
INT64
64-bit integer
Floating-Point Types
FLOAT or REAL
FLOAT64
64-bit floating point
DOUBLE
FLOAT64
64-bit floating point (equivalent to BigQuery’s FLOAT64)
Decimal/Exact Types
DECIMAL or NUMERIC
NUMERIC or BIGNUMERIC
Fixed-point decimal with user-defined precision and scale. BigQuery has an extended BIGNUMERIC for larger precision.
Boolean Types
BOOLEAN
BOOL
True or False
String Types
STRING or VARCHAR
STRING
Variable-length string
CHAR
Not Supported
Fixed-length string is not directly supported in BigQuery; use STRING instead.
Date and Time Types
DATE
DATE
Calendar date (year, month, day)
TIMESTAMP
TIMESTAMP
Timestamp with time zone information
DATETIME
DATETIME
Timestamp without time zone
Syntax difference in Databricks and BigQuery
First_Value :
Databricks
code_block
BigQuery
code_block
<ListValue: [StructValue([(‘code’, ‘FIRST_VALUE (value_expression [{RESPECT | IGNORE} NULLS])\r\nOVER over_clause\r\n\r\nover_clause:\r\n { named_window | ( [ window_specification ] ) }\r\n\r\nwindow_specification:\r\n [ named_window ]\r\n [ PARTITION BY partition_expression [, …] ]\r\n ORDER BY expression [ { ASC | DESC } ] [, …]\r\n [ window_frame_clause ]’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e0e0ed92a30>)])]>
In particular, working with H3 geospatial functions can often present unique translation hurdles. Our resources provide clear mappings, like these:
Databricks Function
BigQuery Equivalent Function
Description
h3_boundaryasgeojson(h3CellIdExpr)
ST_ASGEOJSON(jslibs.h3.ST_H3_BOUNDARY(h3CellIdExpr))
Returns the polygonal boundary of the input H3 cell in GeoJSON format.
h3_boundaryaswkb(h3CellIdExpr)
ST_ASBINARY(jslibs.h3.ST_H3_BOUNDARY(h3CellIdExpr))
Returns the polygonal boundary of the input H3 cell in WKB format.
h3_boundaryaswkt(h3CellIdExpr)
ST_ASTEXT(jslibs.h3.ST_H3_BOUNDARY(h3CellIdExpr))
Returns the polygonal boundary of the input H3 cell in WKT format.
Providing precise details for complex functions like these is crucial. In fact, we’ve found that by detailing these H3 translations, even advanced AI models like Gemini can generate more accurate and reliable BigQuery SQL from your original Databricks Spark SQL, ensuring your geospatial analyses remain intact.
Architecture overview
Before diving into the translation logic, let me show you how the pieces fit together.
Pipeline components
Source SQL Storage:
All original Databricks SQL files were stored in Google Cloud Storage .
Function mapping guide:
A curated guide that maps Databricks-specific SQL functions (e.g., First_value, UCase,etc) to their BigQuery equivalents (FIRST_VALUE, UPPER, TIMESTAMP etc.)
This guide included examples and syntax rules, which were used as input context for Gemini.
Few-shot examples:
I selected a set of hand-translated queries to serve as high-quality training prompts to improve Gemini’s consistency.
Retrieval-Augmented Generation (RAG) layer:
Before querying Gemini, I leveraged the Vertex AI RAG Engine to retrieve relevant function mappings and example translations. This ensured Gemini had grounded knowledge, improving the accuracy of the output. The RAG-enriched prompt was then sent to Gemini for translation, and the returned SQL was optionally post-processed to fix edge cases.
This ensured Gemini had grounded knowledge, improving the accuracy of the output.
Gemini API integration:
The RAG-enriched prompt was sent to Gemini for translation.
Returned SQL was optionally post-processed to fix edge cases.
Validation layer:
Translated SQL queries were validated by executing them in a BigQuery dry run mode to detect syntax issues.
Architecture diagram
Lessons learned
RAG + Gemini = Smart SQL translation: Grounding Gemini with real-world examples and mapping logic made it significantly more accurate.
A comprehensive function mapping guide is essential: Invest time in building a robust function mapping resource.
Thorough validation is the key: Use BigQuery’s dry run and information schema to ensure translated queries are safe and optimized.
Ready to streamline your SQL migrations?
Stop wrestling with SQL syntax and start leveraging the power of your data, wherever it resides. With the Gemini model, we can streamline your Databricks Spark SQL to BigQuery SQL translation process, making it faster, more reliable, and far less painful.
Dive in and accelerate your journey to cross-platform data success. Click on this link to get more details, and take it forward!
AI Summary and Description: Yes
Summary: The text discusses the challenges and solutions involved in migrating SQL workloads between Databricks SQL and Google BigQuery using the Google Gemini AI assistant. It highlights key differences in SQL dialects and emphasizes the importance of function mapping and validation in the migration process.
Detailed Description:
The provided text focuses on the increasingly common practice of migrating SQL workloads across different cloud platforms, specifically from Databricks to Google BigQuery. It emphasizes the challenges posed by the distinct syntax and functions between the two SQL engines and explains how the Google Gemini AI assistant aids in this complex task.
Key Points:
– **Context of Migration**:
– Businesses are diversifying cloud ecosystems, increasing the necessity to migrate SQL workloads between engines.
– The case study involves translating Databricks SQL queries to BigQuery SQL, which involves engaging with diverse SQL dialects.
– **Role of Google Gemini**:
– Gemini assists in transforming SQL queries, which can be complex due to differences in syntax, functions, and execution behavior.
– The text suggests that manual translation is prone to error and inefficiencies, making AI assistance valuable.
– **Data Type Mappings**:
– Provides a detailed comparison of various data types between Databricks and BigQuery, including integers, floating points, decimals, booleans, strings, and date/time types.
– **Function Translation Example**:
– Highlights specific examples where Databricks functions have equivalent functions in BigQuery, emphasizing the need for accurate function translation for complex queries like those involving geospatial functions.
– **Architecture for SQL Translation**:
– The process involves several components:
– Source SQL storage in Google Cloud Storage.
– A curated mapping guide for translating SQL functions.
– Use of Few-shot examples for training AI.
– Integration with Google’s Vertex AI RAG (Retrieval-Augmented Generation) to enhance translation accuracy.
– A validation layer utilizing BigQuery’s dry run mode to check for syntax correctness.
– **Lessons Learned**:
– The combination of RAG and Gemini is particularly effective for SQL translation.
– Building a comprehensive function mapping guide is essential for successful migration.
– Ensuring thorough validation of translated queries is vital for maintaining robustness and performance.
The insights from this process are crucial for professionals in cloud computing and data management, especially those involved in database migrations, AI utilization in data manipulation, and ensuring data integrity during cross-platform transitions. The text serves as a resourceful guideline for mitigating migration challenges and optimizing workflows in cloud ecosystems.