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Ingestion and Historization in the Data Lake

The document outlines the ingestion and historization processes in a data lake, focusing on the use of AWS services such as the Database Migration Service (DMS) and CloudWatch for data management. It discusses architectural designs, ETL processes, and monitoring strategies to handle historical data changes effectively. Achievements include the implementation of automated and scalable processes to support around 70 tables with streamlined metadata management.

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Ingestion and historization in the Data Lake Illia Todor corporate.hrs.com
| HRS | /iamtodor Ingestion and historization in the Data Lake 2 Data Engineer AWS Cloud Practitioner Certified
| HRS | /iamtodor Ingestion and historization in the Data Lake 3 Data Engineer AWS Cloud Practitioner Certified
| HRS | /iamtodor Ingestion and historization in the Data Lake 4 https://github.com/iamtodor https://stackoverflow.com/users/5151861/iamtodor https://iamtodor.medium.com/ https://www.linkedin.com/in/iamtodor/ Data Engineer AWS Cloud Practitioner Certified
HRS | provides the world's premier lodging experience to corporations globally. 50 markets worldwide > 800.000 yearly contacts with Hotels 1,000 employees > 6,000 Global customers 32,000 cities globally
HRS | Lodging as a Service
HRS | Lodging as a Service
HRS | Lodging as a Service
| HRS | ● DataSource ● Historical Changes ● DataLake ● DataWarehouse ● Dashboards Request 9
| HRS | We wanted to move ingestions from DataSource into Data Lake along with applying historization approach. The data we need in the DWH for reporting. The idea of historization approach is to track historical changes. Historization: the process Ingestion and historization in the Data Lake 10
| HRS | Architecture: abstract Ingestion and historization in the Data Lake 11
| HRS | Architecture: in details Ingestion and historization in the Data Lake 12
| HRS | Infrastructure as a Code 13
| HRS | First part: global overview 14
| HRS | First part: DMS 15
| HRS | AWS DMS: Database Migration Service 16
| HRS | AWS DMS: Database Migration Service 17
| HRS | AWS DMS: Database Migration Service 18 AWS Secrets Manager
| HRS | DMS: Replication Instance Ingestion and historization in the Data Lake 19
| HRS | DMS: Target Endpoint Ingestion and historization in the Data Lake 20
| HRS | DMS: Target Endpoint Ingestion and historization in the Data Lake 21
| HRS | DMS: Replication Task Ingestion and historization in the Data Lake 22
| HRS | DMS: Replication Task Ingestion and historization in the Data Lake 23
| HRS | DMS: Replication Task Ingestion and historization in the Data Lake 24
| HRS | DMS: Replication Task Ingestion and historization in the Data Lake 25
| HRS | DMS: Replication Task Ingestion and historization in the Data Lake 26
| HRS | DMS: Replication Task Ingestion and historization in the Data Lake 27
| HRS | First part: DMS 28
| HRS | Process automation and scaling 29
| HRS | First part: Metadata management 30
| HRS | What is metadata itself Ingestion and historization in the Data Lake 31
| HRS | Suitable tool for metadata Ingestion and historization in the Data Lake 32
| HRS | Suitable tool for metadata Ingestion and historization in the Data Lake 33
| HRS | Suitable tool for metadata Ingestion and historization in the Data Lake 34
| HRS | config.json Ingestion and historization in the Data Lake 35
| HRS | config.json Ingestion and historization in the Data Lake 36
| HRS | config.json Ingestion and historization in the Data Lake 37
| HRS | config.json Ingestion and historization in the Data Lake 38
| HRS | config.json Ingestion and historization in the Data Lake 39
| HRS | config.json Ingestion and historization in the Data Lake 40
| HRS | config.json Ingestion and historization in the Data Lake 41
| HRS | First part: Metadata management 42
| HRS | First part: DMS saving data 43
| HRS | DMS: saving data Ingestion and historization in the Data Lake 44
| HRS | DMS: saving data Ingestion and historization in the Data Lake 45
| HRS | DMS: saving data Ingestion and historization in the Data Lake 46
| HRS | First part: DMS 47
| HRS | AWS CloudWatch Ingestion and historization in the Data Lake 48
| HRS | AWS CloudWatch Ingestion and historization in the Data Lake 49 Data Platform product owner
| HRS | AWS CloudWatch Ingestion and historization in the Data Lake 50 Data Platform product owner He is happy when he observes metrics
| HRS | AWS CloudWatch Ingestion and historization in the Data Lake 51 Data Platform product owner He is happy when he observes metrics
| HRS | First part: DMS 52
| HRS | Second part: Historization 53
| HRS | Historization: components Ingestion and historization in the Data Lake 54
| HRS | Historization: components Ingestion and historization in the Data Lake 55
| HRS | Historization: Athena Ingestion and historization in the Data Lake 56
| HRS | Historization: components Ingestion and historization in the Data Lake 57
| HRS | Spark jobs on EMR cluster Ingestion and historization in the Data Lake 58 How it looks like in general
| HRS | Historization: script input params Ingestion and historization in the Data Lake 59
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 60
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 61
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 62
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 63
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 64
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 65
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 66
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 67
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 68
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 69
| HRS | Historization: algorithm Ingestion and historization in the Data Lake 70
| HRS | The workflow is orchestrated by MWAA Workflow management platform Ingestion and historization in the Data Lake 71
| HRS | The workflow is orchestrated by MWAA Workflow management platform Ingestion and historization in the Data Lake 72
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 73
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 74
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 75
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 76
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 77
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 78
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 79
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 80
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 81
| HRS | Airflow: Dag first iteration Ingestion and historization in the Data Lake 82
| HRS | Dag first iteration: Gantt diagramm Ingestion and historization in the Data Lake 83 time tasks
| HRS | Airflow: Dag second iteration Ingestion and historization in the Data Lake 84
| HRS | Airflow: Monitoring and alerting Ingestion and historization in the Data Lake 85
| HRS | Airflow: Monitoring Ingestion and historization in the Data Lake 86
| HRS | Airflow: Monitoring Ingestion and historization in the Data Lake 87
| HRS | Airflow: Monitoring Ingestion and historization in the Data Lake 88
| HRS | Airflow: points to improve Ingestion and historization in the Data Lake 89
| HRS | EMR EC2 Spot Instances Ingestion and historization in the Data Lake 90 Name your own price for EC2 Compute ● A market where price of compute changes based upon Supply and Demand ● When Bid Price exceeds Spot Market Price, instance is launched ● Instance is terminated (with 2 minute warning) if market price exceeds bid price ● Unused On-Demand Instances
| HRS | Airflow: points to improve Ingestion and historization in the Data Lake 91
| HRS | Airflow: ec2 fleet Ingestion and historization in the Data Lake 92 ● The instance fleets configuration for a cluster offers the widest variety of provisioning options for EC2 instances ● With instance fleets, you specify target capacities for On-Demand Instances and Spot Instances within each fleet.
| HRS | That’s it? 93 Ingestion and historization in the Data Lake
| HRS | That’s it? 94 Ingestion and historization in the Data Lake
| HRS | Data type issues Ingestion and historization in the Data Lake 95 ● time_millis
| HRS | Data type issues Ingestion and historization in the Data Lake 96 ● time_millis Spark ParquetSchemaConverter.scala
| HRS | Data type issues Ingestion and historization in the Data Lake 97 ● time_millis Spark ParquetSchemaConverter.scala
| HRS | Data type issues Ingestion and historization in the Data Lake 98 ● time_millis ● inadequate dates
| HRS | Data type issues Ingestion and historization in the Data Lake 99 ● time_millis ● inadequate dates
| HRS | Data type issues Ingestion and historization in the Data Lake 100 ● time_millis ● inadequate dates
| HRS | Data type issues Ingestion and historization in the Data Lake 101 ● time_millis ● inadequate dates
| HRS | Data type issues Ingestion and historization in the Data Lake 102 ● time_millis ● inadequate dates
| HRS | Data type issues Ingestion and historization in the Data Lake 103 ● time_millis ● inadequate dates ● decimal
| HRS | Data type issues: solution Ingestion and historization in the Data Lake 104
| HRS | Data type issues: solution Ingestion and historization in the Data Lake 105
| HRS | DMS: Source Endpoint Ingestion and historization in the Data Lake 106
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 107
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 108 No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 109 DataSource schema: my_schm No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 110 DataSource schema: my_schm No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 111 DataSource schema: my_schm known issue when using DB2 as source with schema name having (_) underscore and less than 8 characters selection rule fails No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 112 DataSource schema: my_schm known issue when using DB2 as source with schema name having (_) underscore and less than 8 characters selection rule fails No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 113 DataSource schema: my_schm known issue when using DB2 as source with schema name having (_) underscore and less than 8 characters selection rule fails No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern We are in process of updating the documentation with this limitation. Being said that there is a workaround to select one specific table by changing "rule-action" from "include" to "explicit"
| HRS | DMS: ReplicationTask fun part Ingestion and historization in the Data Lake 114 DataSource schema: my_schm known issue when using DB2 as source with schema name having (_) underscore and less than 8 characters selection rule fails No tables were found at task initialization. Either the selected table(s) no longer exist or no match was found for the table selection pattern We are in process of updating the documentation with this limitation. Being said that there is a workaround to select one specific table by changing "rule-action" from "include" to "explicit"
| HRS | Further work Ingestion and historization in the Data Lake 115
| HRS | Achievements 116 Ingestion and historization in the Data Lake
| HRS | ● We have built ETL process for full and incremental load data from Data Source to Data Lake Achievements 117 Ingestion and historization in the Data Lake
| HRS | ● We have built ETL process for full and incremental load data from Data Source to Data Lake ● We have automated and auto scaled process Achievements 118 Ingestion and historization in the Data Lake
| HRS | ● We have built ETL process for full and incremental load data from Data Source to Data Lake ● We have automated and auto scaled process ● About 70 tables are running day by day Achievements 119 Ingestion and historization in the Data Lake
| HRS | ● We have built ETL process for full and incremental load data from Data Source to Data Lake ● We have automated and auto scaled process ● About 70 tables are running day by day ● We have just one entry point for adding new tables to the process - all other steps are catching up and reusing this metadata Achievements 120 Ingestion and historization in the Data Lake
| HRS | ● We have built ETL process for full and incremental load data from Data Source to Data Lake ● We have automated and auto scaled process ● About 70 tables are running day by day ● We have just one entry point for adding new tables to the process - all other steps are catching up and reusing this metadata ● We enabled process monitoring and alarming on data and process issues Achievements 121 Ingestion and historization in the Data Lake
| HRS | ● We have built ETL process for full and incremental load data from Data Source to Data Lake ● We have automated and auto scaled process ● About 70 tables are running day by day ● We have just one entry point for adding new tables to the process - all other steps are catching up and reusing this metadata ● We enabled process monitoring and alarming on data and process issues ● Our data stored in effective way and easy to access with different tools Achievements 122 Ingestion and historization in the Data Lake
| HRS | Learning points to you 123 Ingestion and historization in the Data Lake
| HRS | Q&A: Your voice matters 124 Link to the anonymous survey Ingestion and historization in the Data Lake
| HRS | Metadata in DynamoDB Ingestion and historization in the Data Lake 125 Partition key: TableID is the following composition <Source>-<Target>-<SchemaName>-<TableName>
| HRS | Metadata in DynamoDB Ingestion and historization in the Data Lake 126 Partition key: TableID is the following composition <Source>-<Target>-<SchemaName>-<TableName> Sort key: TableName
| HRS | Metadata in DynamoDB Ingestion and historization in the Data Lake 127 Partition key: TableID is the following composition <Source>-<Target>-<SchemaName>-<TableName> Sort key: TableName

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Ingestion and Historization in the Data Lake