SriramGopal
New Contributor II
Joined 7 years ago
User Profile
User Widgets
Contributions
Ingest data from SQL Server (RDBMS) to AWS Cloud Storage (S3)
Contributed by @SriramGopal from Agilisium Consulting The pipeline is designed to fetch records on an incremental basis from any RDBMS system and load to cloud storage (Amazon S3 in this case) with partitioning logic. This use case is applicable to Cloud Data Lake initiatives. This pipeline also includes, the Date based Data Partitioning at the Storage layer and Data Validation trail between source and target. Parent Pipeline Control Table check : Gets the last run details from Control table. ETL Process : Fetches the incremental source data based on Control table and loads the data to S3 Control Table write : Updates the latest run data to Control table for tracking S3 Writer Child Pipeline Audit Update Child Pipeline Control Table - Tracking The Control table is designed in such a way that it holds the source load type (RDBMS, FTP, API etc.) and the corresponding object name. Each object load will have the load start/end times and the records/ documents processed for every load. The source record fetch count and target table load count is calculated for every run. Based on the status (S-success or F-failure) of the load, automated notifications can be triggered to the technical team. Control Table Attributes: UID – Primary key SOURCE_TYPE – Type of Source RDBMS, API, Social Media, FTP etc TABLE_NAME – Table name or object name. START_DATE – Load start time ENDDATE – Load end time SRC_REC_COUNT – Source record count RGT_REC_COUNT – Target record count STATUS – ‘S’ Success and ‘F’ Failed based on the source/ target load Partitioned Load For every load, the data gets partitioned automatically based on the transaction timestamp in the storage layer (S3) Configuration Sources : RDBMS Database, SQL Server Table Targets : AWS Storage Snaps used : Parent Pipeline : Sort, File Writer, Mapper, Router, Copy, JSON Formatter, Redshift Insert, Redshift Select, Redshift - Multi Execute, S3 File Writer, S3 File Reader, Aggregate, Pipeline Execute S3 Writer Child Pipeline : Mapper, JSON Formatter, S3 File Writer Audit Update Child Pipeline : File Reader, JSON Parser, Mapper, Router, Aggregate, Redshift - Multi Execute Downloads IM_RDBMS_S3_Inc_load.slp (43.6 KB) IM_RDBMS_S3_Inc_load_S3writer.slp (12.2 KB) IM_RDBMS_S3_Inc_load_Audit_update.slp (18.3 KB)7.2KViews1like3CommentsIngest data from NoSQL Database (MongoDB) into AWS Cloud Storage (S3)
Contributed by @SriramGopal from Agilisium Consulting The pipeline is designed to fetch records on an incremental basis from document-oriented NoSQL database system (Mongo in this case) and load to cloud storage (Amazon S3) with partitioning logic. This use case is applicable to Cloud Data Lake initiatives. This pipeline also includes, the Date based Data Partitioning at the Storage layer and Data Validation trail between source and target. Parent Pipeline S3 Writer Child Pipeline Audit Update Child Pipeline Control Table - Tracking The Control table is designed in such a way that it holds the source load type (RDBMS, FTP, API etc.) and the corresponding object name. Each object load will have the load start/end times and the records/ documents processed for every load. The source record fetch count and target table load count is calculated for every run. Based on the status (S-success or F-failure) of the load, automated notifications can be triggered to the technical team. Control Table Attributes: UID – Primary key SOURCE_TYPE – Type of Source RDBMS, API, Social Media, FTP etc TABLE_NAME – Table name or object name. START_DATE – Load start time ENDDATE – Load end time SRC_REC_COUNT – Source record count RGT_REC_COUNT – Target record count STATUS – ‘S’ Success and ‘F’ Failed based on the source/ target load Partitioned Load For every load, the data gets partitioned automatically based on the transaction timestamp in the storage layer (S3) Configuration Sources : NoSQL Database, MongoDB Table Targets : AWS Storage Snaps used : Parent Pipeline: MongoDB - Find, Sort, File Writer, Mapper, Router, Copy, JSON Formatter, Redshift Insert, Redshift Select, Redshift - Multi Execute, S3 File Writer, S3 File Reader, Aggregate, Pipeline Execute S3 Writer Child Pipeline: Mapper, JSON Formatter, S3 File Writer Audit Update Child Pipeline: File Reader, JSON Parser, Mapper, Router, Aggregate, Redshift - Multi Execute Downloads IM_NoSQL_S3_Inc_load.slp (29.9 KB) IM_Nosql_S3_Inc_load_S3writer.slp (4.8 KB) IM_Nosql_S3_Inc_load_Audit_update.slp (12.0 KB)5.2KViews0likes1CommentIngest data from Salesforce application into AWS Cloud Storage (S3)
Contributed by @SriramGopal from Agilisium Consulting The pipeline is designed to fetch records from CRM application (Salesforce in this case) via API integration and load to cloud storage (Amazon S3 in this case) with partitioning logic. This use case is applicable to Cloud Data Lake initiatives. This pipeline also includes, the Date based Data Partitioning at the Storage layer and Data Validation trail between source and target. Control Table - Tracking The Control table is designed in such a way that it holds the source load type (RDBMS, FTP, API etc.) and the corresponding object name. Each object load will have the load start/end times and the records/ documents processed for every load. The source record fetch count and target table load count is calculated for every run. Based on the status (S-success or F-failure) of the load, automated notifications can be triggered to the technical team. Control Table Attributes: UID – Primary key SOURCE_TYPE – Type of Source RDBMS, API, Social Media, FTP etc TABLE_NAME – Table name or object name. START_DATE – Load start time ENDDATE – Load end time SRC_REC_COUNT – Source record count RGT_REC_COUNT – Target record count STATUS – ‘S’ Success and ‘F’ Failed based on the source/ target load Partitioned Load For every load, the data gets partitioned automatically based on the transaction timestamp in the storage layer (S3) Configuration Sources: Salesforce Account Targets: AWS Storage Snaps used: Salesforce Read, File Reader, File Writer, Mapper, Router, Copy, JSON Formatter, Redshift Insert, Redshift Select, Redshift - Multi Execute, S3 File Writer, S3 File Reader, Aggregate, JSON Parser Downloads IM_API_Salesforce_to_S3_load.slp (31.6 KB)3.8KViews0likes1CommentIngest data from File Server (FTP/sFTP) into AWS Cloud Storage (S3)
Contributed by @SriramGopal from Agilisium Consulting The pipeline is designed to transfer files from a FTP/SFTP server and load them to cloud storage (Amazon S3 in this case). This use case is applicable to Cloud Data Lake initiatives. This pipeline also includes, the Date based Data Partitioning at the Storage layer and Data Validation trail between source and target. Control Table - Tracking The Control table is designed in such a way that it holds the source load type (RDBMS, FTP, API etc.) and the corresponding object name. Each object load will have the load start/end times and the records/ documents processed for every load. The source record fetch count and target table load count is calculated for every run. Based on the status (S-success or F-failure) of the load, automated notifications can be triggered to the technical team. Control Table Attributes: UID – Primary key SOURCE_TYPE – Type of Source RDBMS, API, Social Media, FTP etc TABLE_NAME – Table name or object name. START_DATE – Load start time ENDDATE – Load end time SRC_REC_COUNT – Source record count RGT_REC_COUNT – Target record count STATUS – ‘S’ Success and ‘F’ Failed based on the source/ target load Partitioned Load For every load, the data gets partitioned automatically based on the transaction timestamp in the storage layer (S3) Configuration Sources: FTP/sFTP File Extracts Targets: AWS Storage Snaps used: File Reader, File Writer, Mapper, Router, JSON Formatter, Redshift Insert, Redshift Select, Redshift - Multi Execute, S3 File Writer Downloads IM_FTP_to_S3_load.slp (15.3 KB)5KViews0likes1Comment