Technology

A very popular on-premises relational database system is Oracle. The database can be optimized for use with a variety of workloads such as data warehouse loads, mixed loads, and transactions. One of the main advantages of the Oracle database is that it is ideal as an analysis tool and for managing ETL pipelines as a comprehensive querying layer. Critical applications can be easily operated on Oracle because of its inherent stability and short recovery times.

A very common factor in the ETL ecosystem is the need for Oracle database replication to another database. It is done to combine data from Oracle with other sources to maximize data analytics.

Before going into the many aspects of Oracle database replication, it is necessary to understand what is database replication. In a nutshell, it is the process where data from one source is replicated and stored in different locations, thereby increasing data accessibility across the network. Database replication is flexible and is very effective in both servers and standalone computers. This data can be stored in a range of options, from cloud-based hosts to on or off-site, and even within the same system. Almost all database technologies such as Microsoft SQL Server or Oracle database have in-built features for replication. In other instances, specialized database replication software is used.

The most technologically advanced tools and methods are used for Oracle database replication.

·        In the first method, a snapshot of the needed format is generated by taking the full dump of the table to be replicated at fixed periods. The dump file generated is then used to load another table into a different data warehouse or to execute any other customized transformation. However, the shortcoming of this method is that it is effective in the case of small tables but then, it can be also used for tables in an ETL.

·        In the second method, when pre-set events occur, configured triggers are executed. One of the trigger configurations is when a table is modified in Oracle database replication. At this time, a record is inserted into another table which is then used to replicate the Oracle data to a target database through a custom code. This trigger-based method is synchronous process because unless the triggers are completely carried through, the transactions in the database will not be successful.

·        Finally, there is the Oracle Golden Gate Change Data Capture (CDC). It is the solution for all things that are related to ETL for Oracle database replication. Golden Gate is based on binary logs that are historical data of all changes made in an Oracle database since its creation. The key benefit of this method is real-time support to a large number of target databases. The handlers facilitate the data transformation when a target database is loaded.

One of the most popular on-premises relational database systems is Oracle. It can be used for a variety of workloads such as data warehouse loads, mixed loads, and transactions. The advantage of Oracle is that it is ideal as an analysis tool and for managing ETL pipelines as a comprehensive querying layer. Because of its inherent stability and short recovery times, critical applications can be easily run on Oracle.

A very common factor in the ETL ecosystem is the need for Oracle database replication to another database. It is done to combine data from Oracle with that from other sources to maximize data analytics and results.

What is database replication?  

It is the process where data from one source is replicated and stored in different locations, thereby increasing data accessibility across the network. Database replication is very effective in both servers and standalone computers and this data can be stored in cloud-based hosts, on or off-site, or within the same system. Almost all database technologies such as Microsoft SQL Server or Oracle database have in-built features for replication. In other instances, specialized database replication software is used.

For Oracle database replication, special tools and methods are used. The most widely used ones are:.

·        In the first method, the full dump of the table to be replicated is taken at fixed periods to generate a snapshot of the needed format. The dump file generated is then used to load another table into a different data warehouse or to execute any other customized transformation. One downside of this method is that it is effective in the case of small tables but then, it can be also used for tables in an ETL.

·        In another method, triggers are configured to be executed when pre-set events occur. One of the trigger configurations is when a table is modified in Oracle database replication. At this time, a record is inserted into another table which is then used to replicate the Oracle data to a target database through a custom code. This method based on a trigger is a synchronous process because unless the triggers are completely carried through, the transactions in the database will not be successful. This problem though can be skirted by using a multi-master replicated instance of Oracle as the source database.

·        Finally, there is the Oracle Golden Gate Change Data Capture (CDC). It is the solution for all things that are related to ETL for Oracle database replication. Golden Gate is based on binary logs that are historical data of all changes made in an Oracle database since its creation. The main benefit here is that it provides real-time support to a large number of target databases through its handlers and facilitates the transformation of data while loading a target database.

Amazon Web Service Data Migration Service (AWS DMS) is a cloud-based service that is used to migrate databases, data warehouses, and NoSQL databases via the CDC feature of AWS. It is also easy to migrate databases from an on-premises server to the cloud, from a cloud-based platform to another similar provider, and even in rare cases, from the cloud to on-premises.

AWS data migration helps migrate databases from a target source to AWS seamlessly. During migration, the database at source remains functional and businesses do not have to experience any stoppage of services or downtime.

With AWS data migration, you can also carry out homogeneous migration like Oracle to Oracle or heterogeneous migration from Microsoft SQL Server or Oracle to Amazon Aurora based in the cloud. It can consistently replicate data with high availability to consolidate databases into a data warehouse by streaming data to Amazon S3 and Amazon Redshift. New users of AWS DMS can avail of free services for six months for migrating data to Amazon Redshift, Amazon Aurora, Amazon DynamoDB, or Amazon Document DB.

Migrating databases with AWS is not a difficult and complex process provided the right and advanced cutting-edge tools are used. One is the ETL tool for AWS data migration which is fully automated and does not require any form of human intervention.

Broken down into its components ETL for data integration comprises of the following steps.  

Extract– The required data is mined (extracted) from Homogeneous or Heterogeneous Data sets

Transform – The data so extracted is transformed/modified to the required format to match the storage facility architecture.

Load – Loading or migrating data to the target database that includes Data Marts or Data Warehouse

Considering the above, the ETL tool for AWS data integration is a three-step process. The data sources can be from various points but for a cloud-based platform like the AWS ecosystem, they are Aurora, S3, Relational Database Service (RDS), and EC2.

There are several benefits of AWS Data Migration Service.

·        Easy to use – With DMS AWS, there is no need for installing additional drivers or applications and changes to the source database are also not required.

·        No downtime – During migration, changes to data at the source are continuously replicated to the target. This ensures that the source database remains functional during migration.

·        Affordability – DMS AWSis a low-cost service and payment is only for the volume of computing or storage resources used.

·        Supports commonly used databases – DMS AWSsupports the most widely-used databases in open-source or commercial applications

·        Quick setup – A migration activity can be started within minutes in the AWS Management Console and only the parameters have to be defined in the DMS AWS

·        Dependability – DMS AWS continually tracks source and target databases, replication instance, and network connectivity and takes corrective action whenever required during migration.

Data in the present business ecosystem is the workhorse for any organization. The data is from various sources such as traditional databases, application-generated files, and backups to modern machine-generated IoT and network device data. Even the data from the smart coffee maker helps to decide profitability and revenue. Hence, there is a need for cost and operationally efficient ways to store and access the data and enable integration paths for data that helps in creating data lakes for gaining detailed insights through analytics.

Data ingestion AWS (Amazon Web Service) is used to simplify data transfer mechanisms and enable data migration. This is more so when businesses adopt new data storage services like the cloud-based AWS to increase performance, scalability, and operational efficiency. Businesses want to move away from complexities of custom tooling and scripts to more repeatable design patterns.

Why do businesses prefer data ingestion AWS and transfer data to the cloud.

·        Application data migration – Data is migrated into AWS so that the benefits of fully managed AWS file services can be leveraged to increase agility and operational efficiency.

·        Data lakes – With data ingestion AWS and centralizing data into the cloud, Amazon S3 storage service can be used to build data lakes and centralize data processing abilities. Greater value is therefore derived from the aggregated data.

·        Data sharing – Productivity and business value are increased by sharing data at local and global levels.

·        Backups – Businesses are storing cost-effective and highly durable backups of their data in Amazon S3 to adhere to compliance requirements.

·        Archiving data –There is an increasing need to archive massive amounts of long-term retention-based data to AWS. This is because organizations want to give up old, costly, and complex on-premises storage systems so that more cost savings and operational efficiencies may be leveraged.

Despite the many benefits, there are several potential challenges to data ingestion AWS. There are many variables to consider. Is it preferable to transfer data online or offline or a combination of the two? Which option is best suited in your case for data transfer timeframe that meets your specific requirements?

You have to also consider which mode of transferring data is the fastest and the simplest and which method can integrate and scale to meet different volumes and dataset characteristics. Then there is the time and effort required for data ingestion AWS to build and test bespoke data transfer code. You have to decide too who is going to maintain and write it for different use cases. Creating a repeatable mechanism is quite challenging.

Amazon Web Service Data Migration Service (AWS DMS) is a service that is cloud-based used to migrate relational databases, NoSQL databases, and data warehouses through the change data capture (CDC) feature of AWS DMS. It is also used to migrate databases from on-premises to cloud provider, between on-premises system or a combination of both. AWS DMS has all the advanced capabilities that are associated with cloud-based services such as high security, data flexibility, speed to market, and cost affordability.

AWS data migration facilitates the migration of databases from any target source to AWS quickly and efficiently. The benefit is that during the process of migration the source database remains fully functional and hence businesses do not have to suffer a break in activity or downtime. All databases can have data migrated from or to them with AWS DMS.

Both homogenous migration like Oracle to Oracle or heterogeneous migration like Oracle or Microsoft SQL Server to cloud-based Amazon Aurora can be carried out with AWS data migration. It can continually replicate data with high availability to consolidate databases into a data warehouse. This is done by streaming data to Amazon S3 and Amazon Redshift. DMS AWSis free for six months for users migrating data to Amazon Redshift, Amazon Aurora, Amazon DynamoDB, or Amazon Document DB.

The main benefits of AWS data migration service are as follows.

·        User-friendly –Organizations can do away with installing additional drivers or applications without making changes to the source database when using AWS data migration. The process can be initiated directly on the AWS Management Console. The data migration service automatically manages the complexities of data migration and replicates the data located in the source database.

·        No break in service – With AWS DMS data changes at the source is replicated to the target without a break during migration with the source database remaining functional during the migration. Hence, there is no break in service or downtime during migration.

·        Supports popular databases - AWS data migration service supports the most commonly-used databases in open-source commercial applications. This includes both homogenous and heterogenous migration. It can also migrate data between SQL, NoSQL, and text-based targets.

·        Cost-effective – AWS DMS is a highly affordable option for businesses. Payment is made only for the quantum of computing and storage resources used without any fixed charges. For example, $3 is the cost of migrating a terabyte-sized database, regardless of the type of databases being migrated.

·        Reliability – During migration, AWS data migration monitors source and target databases continually as well as replication instance, and network connectivity and takes quick corrective action if required. If the process is interrupted at any point, it starts automatically again from where it was stopped.

·        AWS DMS can be started quickly in the AWS Management console after the parameters are defined.