odbc2deltalake 0.9.0

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ODBC 2 Deltalake

This is a package that supports reading from ODBC and writing to a scd2 DeltaTable. The delta table will always have the following "system" cols:

• __timestamp : The Date of the Load
• __is_deleted : True for a deletion of a record
• __is_full_load : True if it was a full load, meaning an implicit deletion of not delivered records

Currently, this package is focused very much on Microsoft SQL Server. But it should not be too hard to add support for other DB Systems.

This packages handles delta's based on MS-SQL's "timestamp"/"rowversion" DataType or based on Temporal Tables. Other methods will be added later on. It does detect updates to records that happened differently, eg if a restore happened in the source.

Usage

First, pip install it: pip install odbc2deltalake[local,local_azure]

It's as simple as this:

from odbc2deltalake import write_db_to_delta

write_db_to_delta(
    your_odbc_connection_string,
    (your_db_schema, your_table_name),
    Path("YOUR_TARGET_PATH")
)

Usage with Databricks

You can also use databricks instead of the default delta-rs/duckdb implementation. In that case you can do pip install odbc2deltalake without any dependencies other than Databricks Spark/DBUtils

from odbc2deltalake.destination.databricks import DatabricksDestination
from odbc2deltalake.reader.spark_reader import SparkReader
from odbc2deltalake import write_db_to_delta


dest = DatabricksDestination(dbutils=dbutils,container="raw", account="some_account", path="raw/super_path", schema="abfss")
reader = SparkReader(spark=spark, sql_config={ # see https://docs.databricks.com/en/connect/external-systems/sql-server.html
    "host": "some_host",
    "port": "1433",
    "user": "some_user",
    "encrypt": "true",
    "password": "what_ever",
    "database": "mypage"
}, spark_format="sqlserver")

write_db_to_delta(reader, ("dbo", "user"), dest)

Advanced Scenarios

See class WriteConfig, of which you can pass an instance to write_db_to_delta

class WriteConfig:

    dialect: str = "tsql"
    """The sqlglot dialect to use for the SQL generation against the source"""

    primary_keys: list[str] | None = None
    """A list of primary keys to use for the delta load. If None, the primary keys will be determined from the source"""

    delta_col: str | None = None
    """The column to use for the delta load. If None, the column will be determined from the source. Should be mostly increasing to make load efficient"""

    load_mode: Literal["overwrite", "append", "force_full"] = "append"
    """The load mode to use. Attention: overwrite will not help you build scd2, the history is in the delta table only"""

    data_type_map: Mapping[str, ex.DATA_TYPE] = dataclasses.field(
        default_factory=lambda: _default_type_map.copy() # defaults to some simple sql server related maps
    )
    """Set this if you want to map stuff like decimal to double before writing to delta. We recommend doing so later in ETL usually"""

    get_target_name: Callable[[InformationSchemaColInfo], str] = dataclasses.field(
        default_factory=lambda: compat_name # defaults to removing spaces and other characters not allowed by spark
    )
    """A method that returns the target name of a column. This is used to map the source column names to the target column names.
    Use if you want to apply some naming convention or avoid special characters in the target. """