Oracle Ingestion

Fetch Data Using

The mechanism for Infoworks DataFoundry to fetch data from the database.

Connection URL

The JDBC URL for Infoworks to connect to the database. The URL must be in the following format: jdbc:oracle:thin://<ip>/TMODE=<tmode>,database=<databasename>

Username

The user name for database connection.

Password

The password for the username provided.

Source Schema

The database schema to be crawled.

ECB Agent

The ECB agent for the source. This field is displayed if ECB agent is enabled for the source.

Ingest Type

The types of synchronization that can be performed on the table.

The options include Full Load, Timestamp-Based Incremental Ingestion, Query Based Incremental Ingestion, Batch ID Based Incremental Ingestion,Log-Based Incremental Ingestion, OGG Based Incremental Ingestion.

Segmented Load Status

The option to crawl large tables by breaking into smaller chunks. The smaller chunks can be crawled in parallel. Select the column to Perform Segmented Load On. To derive a column, check the Use substring from field option and select the Extract function. After completing the Segmented Load, ensure to set this value to Completed to submit Incremental Load or Full Load jobs.

NOTE: In case of date, datetime, timestamp column types you can extract new columns. The possible extraction options are year, month, year-month, month-day and day-num-in-month.

Source Configuration

Natural Keys

The key to identify the row uniquely. This key is used to identify and merge incremental data with the existing data on target. Distribution of data into secondary partitions for a table on target will be computed based on the hashcode value of the natural key.

This field is mandatory for incremental ingestion tables.

NOTE: The value of natural key column cannot be updated when updating a row on source; all the components of the natural key are immutable.

Split by

The option to crawl the table in parallel with multiple connections to database.

Split-by column can be an existing column in the data or derived from an existing column. The numbers, date, datetime and timestamp columns are allowed to be selected as split-by key. In general, any column for which min and max can be computed can be a split-by key.

NOTE: Keys which increase or decrease gradually can be used as split-by.

To derive a column, check the Use substring from field option and select the Extract function.

Enable Boundary Query

The query to use for creating splits. This feature is supported for ECB based RDBMS source also. For details, see the Boundary Query section in the Advanced Ingestion Functionalities.

Wide Table

The wide table field must be enabled if table DML statements on the source tables exceed the length of 4000 bytes. This field is available only available for log-based incremental load.

Synchronization Configuration

CDC Start Date

The option to fetch a specific portion of the delta, based on the time window. The start date of the time window.

CDC End Date

The option to fetch a specific portion of the delta, based on the time window. The end date of the time window.

Enable Schema Synchronization

The option to synchronize the Hive table schema, if the source table is modified after the table ingestion is complete.

Timestamp-Based Incremental Load

Timestamp Column for Insert

The source column to be used as insert watermark.

Timestamp Column for Update

The source column to be used as update watermark.

NOTE: It can also be the source column to be used as insert watermark.

Query-Based Incremental Load

Slowly Changing Dimension Type

The type of the query based ingestion. The options include SCD Type 1 and SCD Type 2. SCD Type 1 always overwrites the existing record in case of updates.

Configure Insert Query

The option to input the query which identifies and fetches the incremental insert records from the source table, using the record insertion row from the audit table.

Join Column for Insert

The column used to identify the inserts. This column will be joined with audit table column. This field uses source datatype.

Configure Update Query

The option to input the query which identifies and fetches the incremental update records from the source table, using the record update row from the audit table.

Join Column for Update

The column used to identify the updates. This column will be joined with audit table column.

External Audit Schema Name

The schema name of the audit table where the timestamps are being stored.

External Audit Table Name

The name of the audit table where the timestamps are being stored.

External Audit Join Column

The column by which the data table and the audit table will be joined.

External Audit Timestamp Column

The timestamp column in the audit table, which maintains record audit.

BatchID-Based Incremental Load

Batch-ID Column

This column used for fetching of delta. This column must be a numeric column. Source datatype and target datatype must be equal.

Start Batch Id

The numeric value of Batch ID column (configured by the user during table configurations) starting which the delta is fetched.

End Batch Id

Numeric value of Batch ID column (configured by the user during table configurations) till which the delta is fetched.

OGG-Based Incremental Ingestion

Username

The username to authenticate the replication database.

Password

The password for username provided.

Connection URL

The url to connect to the replication database.

Source Schema

The schema in the replication database where the captured table is present.

Captured Table Name

The name of the table where the actual table data will be replicated.

Target Configuration

Hive Table Name

The name of the table in Hive which will be used to access the ingested data.

Storage Format

The format of the data file to be stored in HDFS. The options include ORC and Parquet.

Partition Hive table

The option to partition the data in target. The partition column also can be derived for date, datetime and timestamp column. This will further partition the data. A hierarchy of partitions are supported with both normal partitions and derived partitions.

NOTES: _Ensure that the partition columns data is immutable._You can also provide a combination of normal and derived partitions in the hierarchy.

Number of Secondary Partitions

The number of secondary partitions to run the MR jobs in parallel. The target data can be distributed among various partitions and each partitions in turn can have various secondary partitions. A table with no primary partition can also have secondary partitions. Secondary partition helps in parallelising the ingestion process.

Number of Reducers

The number of reducers for the ingestion map reduce job. Increasing the number of reducers helps reduce the ingestion duration.

This will help in processing the MR jobs faster. This will be effective with the combination of partition key and number of secondary partitions. In any data processing using MR job, this will help in bringing the parallelism based on the data distribution across number of primary partitions and secondary partitions on Hadoop.

Generate History View

The option to create history view table (along with the current view table) in Hive, which contains the versions of incremental updates and deletes.

Number of Secondary Partitions to Merge in Parallel

The number of secondary partitions that can be merged in parallel for a table.