richardavisstuff - Tumblr blog

[richardavisstuff]

Using the ETL Tool in AWS Database Migration

AWS (Amazon Web Service) is a cloud-based platform providing infrastructure, platform, and packaged software as a service. However, the most critical service of Amazon Web Service Database Migration Service (AWS DMS) is migration of data between relational databases, data warehouses, and NoSQL databases for which ETL in AWS is the most optimized tool.

To understand why ETL in AWS is preferred for migrating databases, one should know how the ETL tool works. ETL is short for Extract, Transform, Load. Through these three steps data is combined from several sources into a centralized data warehouse. In the ETL in AWS process, data is extracted from a source, transformed into a format that matches the structure of the target database, and then loaded into a data warehouse or a storage repository.

The Benefits of ETL in AWS

There are several benefits of ETL in AWS for database migration.

·        Organizations need not install and configure additional drivers and applications or change the structure of the source database when migration is done with ETL in AWS. The process is initiated directly through the AWS Management Console whereby any change or update is replicated to the target database through its Change Data Capture (CDC) feature.

·        Changes that occur in the source database are updated at regular intervals to the target database by ETL in AWS. However, for this to happen, the source and the target databases should always be kept in sync. Most importantly, the migration process can take place even when the source database is fully functional. Hence, there is no system shutdown, a big help for large data-driven enterprises where any downtime upsets operating schedules.

·        Most common and popular databases in use today are supported by AWS. Hence, ETL in AWS can handle any migration activity regardless of the structure of the databases. These include both homogeneous and heterogeneous migration. In the first, the database engines, data structure, data type and code, and the schema structures of the source and the target databases are similar while in the second case, each is different from the other.

·        ETL in AWS is also widely-used for migrating on-premises databases to Amazon RDS or Amazon EC2, Aurora, and databases running on EC2 to RDS or vice versa. Apart from these possibilities, database migration can be done between SQL, text-based data, and NoSQL with DMS AWS also.

High-Performing AWS Glue

There are various tools for ETL in AWS, but the most high-performing one is AWS Glue. It has a fully managed ETL platform that eases data processing for analysis. The tool is very user-friendly and can be set up and running with only a few clicks on the AWS Management Console. The advantage here is that AWS Glue discovers data automatically and stores the connected metadata in the AWS Glue Data Catalog. Later, the data can be searched and queried instantly.

[richardavisstuff]

The Need and Uses for Oracle Database Replication

Database Replication is the process where data is stored in multiple locations so that the current database version can be accessed from any remote location. This requires data to be copied from one server to another for sharing across regions. Data Replication is mainly used for disaster recovery where an outage in one location automatically triggers servers in secondary locations so that the work is not disrupted. Further, placing a backup of the data close to the user quickens speeds and reduces network loads.

This post will detail Oracle database replication and its benefits.

Oracle has been the mainstay of database management for several decades as it increases operational efficiencies and one of the most optimized ways for it is through Oracle data replication. It is a process where data access is facilitated through several sources like servers and sites. Since real-time data access is permitted, Oracle database replication has high data availability.

The Need for Oracle database replication?

Oracle database replication ensures seamless sharing, distributing, and consolidating of data and enables organizations to share it with partners and vendors and keep it in sync across various locations. This data is accessible in branches and remote locations, wherever they might be located. Companies use Oracle database replication for creating multiple copies of their database that are all synchronized for backups in case of disaster recovery, testing, distributed data processing, and business reporting.  

Benefits of Oracle database replication

Several benefits can be had by businesses through Oracle database replication.

The most important is improved server performance since businesses always channel data read functions to a replica during the process of database replication. This helps DBAs and other authorized IT personnel to minimize processing cycles on the primary server and make it the main tool for write operations.

The next critical benefit provided by Oracle database replication is that it enhances application availability as the replication copies data to several servers in various locations. Hence, data from applications can be easily accessed even if one of the servers in any remote location faces an outage or disruption due to faults in hardware or a malware attack. In such cases, the secondary servers are triggered and the data is not lost.

Finally, Oracle database replication assures improved network performance. This is because minimum data access latency is had as multiple copies of the same data are maintained in various locations. For example, users in “A” country might face latency issues when accessing data in “B” country, an issue that can be resolved by Oracle database replication as a replica of the data can be placed close to the user’s location. Another very relevant instance is e-commerce sites where visitors from various countries view the same product information at each site because of Oracle database replication.

These are some of the benefits of Oracle database replication.

[richardavisstuff]

ETL Tools for AWS Simple Storage Service (S3)

Amazon Web Service (AWS) has become very popular among organizations all over the world. It is a computing platform that is based in the cloud and provides services on a pay-as-you-go basis. Its other advanced and cutting-edge functionalities include increased storage capabilities, proficient workload handling, quick server access, and many more.

One of them that will be discussed in detail in this post is the Simple Storage Service (S3) of AWS. This storage service of Amazon enables users to access secure and scalable data storage regardless of the format – codes, documents, backups, and weblogs. S3 also offers high data availability and durability and ETL tools for S3 that match well with most programming languages for reading, writing, and transforming data.

What is the functioning of ETL (Extract, Transform, Load) tools and how do they work in the S3 context?

An ETL tool is mainly used when there is a need to combine several databases or data warehouses regardless of their location into one data storage facility. The functioning of ETL tools for S3is quite simple. First, data is extracted from the source database that is then processed and formatted to match the data structure of the target database. The final step is to load the formatted data into the intended target database. This complete process of migration does not require any human intervention as ETL tools for S3are fully automated, ensuring zero data loss from errors, optimized database performance, and greater cost efficiencies.

Why should you use ETL tools for S3and what are the benefits?

The most critical benefit of using ETL tools for S3is that the extracting, transforming, and loading functions for moving data into a centralized data warehouse are fully automated. Hence, organizations can depend on quick and reliable data analytics for faster decision-making as data can be collated from S3 and other sources to gain better business insights. Time and effort spent on creating custom scripts or troubleshooting upstream data issues are also lower.

How do ETL tools for S3work?

In the data extract phase, users can collect data from an S3 bucket or by selecting specific directories to do so with the ETL tools for S3. Existing files in the target database or directory are then updated with any changes made at the source with the very effective Change Data Capture (CDC) feature of the tool.

For the next stage, ETL tools for S3analyze the data architecture of the source and the target database and if found different, the source database structure is formatted to match that of the intended database. The function is carried out by an auto-generated transformation script.

Finally, the tool loads the data and auto-configures tables and schemas in Redshift or Snowflake to support the S3 data and optimizes the process of database migration.  

[richardavisstuff]

Data Migration with DMS AWS – Types and Functions

The Database Migration Service of Amazon Web Service - DMS AWS – is a cloud-based platform primarily used to migrate databases from on-premises servers to the cloud or from one cloud provider to another. It is also used to move data between NoSQL databases, relational databases, and data warehouses. Migration can either be a one-time activity where an entire database is moved from the source to the target or continual replication of changes made at the source to the target provided both are always kept in sync.

Migrating databases with DMS AWS

There are two types of database migration to the cloud with DMS AWS depending on the structure of the source and the target databases.

Homogeneous Database Migration

This form of migration is carried out when the source and the target database engines, data types, data codes, and the schema structures are the same. The entire database can be migrated at one-time but for that to happen, a link has to be established first between the source and the target. This allows DMS AWS to know from where the database has to be migrated and to which location.

For homogeneous migration, the source database has to run either on an Amazon EC2 instance located outside the AWS or on an Amazon RDS database. The target database has to run on an Amazon RDS or an Amazon EC2 instance. Some examples of homogeneous database migration are MySQL to Amazon Aurora, Microsoft SQL Server to Amazon RDS for SQL Server, Oracle to Amazon RDS for Oracle, and MySQL to Amazon RDS for MySQL.

Heterogeneous Database Migration

This type of migration is done when the parameters of the source and the target databases- database engines, data types, data codes, and schema structures – are different. The migration process here is a two-step one. First, the data code and the schema structures of the source database have to be processed and formatted to match that of the intended target database. Once this step is concluded, the usual migration process with DMS AWS may be initiated. Both the conversion and the migration are auto-handled by DMS.

As in homogeneous migration, the source database can be on an Amazon EC2 instance or an Amazon RDS database that is located on-premises or off-site. The target database may be on an Amazon RDS or Amazon EC2 instance. Instances of heterogeneous migration include Microsoft SQL Server to MySQL, Oracle to Amazon Aurora, and Oracle to PostgreSQL.

Organizations needing to migrate databases with DMS AWS have to select one of the two methods depending on the structure of the databases.

The critical benefit of DMS AWS is that it is a fully-managed service. It deploys, manages, and tracks all the needed infrastructure so that migration can be set up and running within minutes once DMS is configured. In comparison, traditional migration processes were time-consuming affairs.  

[richardavisstuff]

Migrating Databases with Amazon Database Migration Service

The Amazon DMS (Database Migration Service) is one of the most optimized cloud-based platforms to migrate databases from on-premises systems to the cloud or from one cloud provider to another. You can choose one-time migration of databases or continuous data replication provided the two databases are always kept synchronized. Users can use Amazon DMS to move data from relational databases, NoSQL databases, or data warehouses.

Amazon DMS cloud-based replication software functions only when a link is established between the source and the target database so that it can know from where the data has to be moved and to which location. The next activity is defining the task to migrate data from the source to the target. The software is completely automated and the migration activity can be completed without any human intervention even in cases where the keys and the tables that are required for migration are not existing in the target database.

However, there is a rider here for Amazon DMS. In such instances, the AWS SCT (AWS Schema Conversion Tool) has to be used for creating views, indexes, tables, and triggers in the target database. Users get all the benefits of a cloud-based environment like data usage elasticity, improved performance and security, and cost efficiencies.

Types of Migration with Amazon DMS

There are two types of migration possible with Amazon DMS depending on the structure and the build of the databases.

Homogeneous Database Migration

This type of database migration with Amazon DMS is done when the database engines, the data codes and types, and the schema structures of the source and the target databases are similar. This migration is a simple one-step job as the full database is migrated at a time after a link is created between the source and target. It has to be ensured also that the source database is operating either on Amazon EC2 instance or an Amazon RDS database. Examples of Amazon DMS homogeneous database migration are Oracle to Amazon RDS for Oracle, MySQL to Amazon RDS for MySQL, MySQL to Amazon Aurora, and Microsoft SQL Server to Amazon RDS for SQL Server.

Heterogeneous Database Migration

In this case, the database engines, data types and codes, and the schema structures of the source and the target databases are different.

Hence, the migration is done in two stages. In the first step, users have to convert the schema structure and the data code into a format that matches the architecture of the target database. Only after completing this stage can the migration process be initiated with Amazon DMS.

As in Homogeneous Migration, the source database can be on an Amazon EC2 instance or an Amazon RDS database. Examples of heterogeneous migration are Oracle to PostgreSQL, Microsoft SQL Server to MySQL, and Oracle to Amazon Aurora.

For database migration, any one method has to be selected.

[richardavisstuff]

The Functioning of Database Migration with AWS

The Database Migration Service of Amazon Web Service (DMS AWS) is a highly optimized solution for migrating databases from on-premises servers to the cloud or from one cloud provider to another. Businesses have the option of selecting between one-time migration or continuous replication of change data through the Change Data Capture feature provided both the source and the target databases are always kept in sync. DMS AWS is also the ideal platform to move data between relational databases, data warehouses, or NoSQL databases.

The functioning of DMS AWS

DMS AWSis a cloud-based replication software that functions only when connectivity is established between the source and the target database. This is necessary as DMS has to know from where to migrate the data and to which intended database. After the linking is done, the activity that will help in loading data from the source to the target has to be clearly marked out.

A critical advantage of data migration with DMS AWSis that the entire process is automated and does not require human intervention at any stage, even when the keys and the tables required for migration are not present in the target database. However, in such instances, users have to create tables, indexes, views, and triggers in the target database with the AWS Schema Conversion Tool (AWS SCT).

After the data is migrated, businesses get all the benefits inherent in a cloud ecosystem. These include high data security, cost-efficiency, usage elasticity, and improved database performance.

Types of database migration with DMS AWS

DMS AWSprovides two types of database migration.

The first is Homogeneous Database Migration. This method is deployed when the database engines, data types and codes and the schema structures of both the source and the target databases are the same. This is a simple one-step process where after establishing a link the entire database is migrated in one shot from the source to the target.

In Homogeneous Migration, the source database should either operate on an Amazon EC2 instance or an Amazon RDS database. Some examples of this form of migration with DMS AWSare Oracle to Amazon RDS for Oracle, MySQL to Amazon RDS for MySQL, MySQL to Amazon Aurora, and Microsoft SQL Server to Amazon RDS for SQL Server.

The second type is the Heterogeneous Database Migration. Here, unlike the first method, the database engines, data codes and types, and the schema structures of the source and the target databases are different. The migration here is a two-step one and a bit complex. In the first step, the AWS SCT tool is used to convert the schema structure and the data code of the source database to one that complements the target database. The migration process can then proceed as in Homogeneous Migration.

The method is selected by businesses for database migration depends on the structure of the databases.

[richardavisstuff]

undefined

Amazing (Eastern) Switzerland

[richardavisstuff]

AWS Database Migration with the ETL Tool

Cloud-based Amazon Web Service (AWS) has several cutting-edge features that are inherent in a cloud ecosystem. These include unlimited storage capacities, optimized content delivery, and high-performing computing powers. Each of these attributes can be categorized into different silos to enhance business convenience.

A very critical component of AWS is the Database Migration Service (DMS) that facilitates the migration of databases from on-premises systems to the cloud or from one cloud provider to another as well as between data warehouses, relational databases, and NoSQL databases. To make sure that these activities are carried out at peak efficiencies and accuracy, the most preferred method is the use of the AWS ETL tool.

ETL is an acronym for Extract, Transform, Load, and the process is primarily used to combine several data warehouses or databases into a single and centralized data storage facility. The AWS ETL tool for database migration works as follows. It first extracts data from the source database, Transforms the data into a format that matches the data architecture of the target database, and finally Loads the processed data into the intended database. This procedure is not prone to human errors or data loss and leads to high-performing databases as the AWS ETL tool is fully automated.

Why should you use the AWS ETL tool?

There are several benefits of the AWS ETL tool that make it the preferred option for organizations wanting to migrate databases to the cloud.

·        The first and the most critical benefit is that businesses do not have to install or configure additional drivers while migrating databases with the AWS ETL tool. Further, no changes have to be made to the source database either. The complete process can be started and done directly through the AWS Management Console. It easily replicates also any changes made at the source database through the Change Data Capture feature.

·        The AWS ETL tool identifies all changes and updates made at the source database and continually updates them to the target database at pre-determined intervals. However, the source and the target databases must always be kept in sync if this is to happen. Moreover, the source database is always functional during migration, a big convenience for large data-driven enterprises where shutting down systems for any length of time can badly impact operations.  

·        Migration of databases can be carried out with the AWS ETL tool regardless of their structure as AWS supports almost all popular common ones. There are two types of migration that can be carried out with the tool – Homogeneous Database Migration and Heterogeneous Database Migration.

·        The AWS ETL tool facilitates migration between on-premises databases to Amazon RDS or Amazon EC2, Aurora, and databases running on EC2 to RDS or vice versa. AWS also migrates databases between SQL, text-based data, and NoSQL.

These are some of the several advanced benefits of ETL AWS.

[richardavisstuff]

undefined

[richardavisstuff]

Sessizliğinde kaybolup gittiğiniz doğal bir yaşam içinde tükenip bittiğiniz kalabalık ve insani kargaşalardan daha iyidir..

[richardavisstuff]

Amazon S3 as a Target for AWS Database Migration Service

Migrating databases from on-premises systems to the cloud or from one cloud provider to another has never been simpler than using the Amazon Web Service Database Migration Service (AWS DMS). The critical benefit here is that for migration, no additional drivers or applications need to be configured and installed as the movement of data from the source to the target databases can be initiated with a few clicks on the AWS Management Console.

An example here is data migration to Amazon S3 (Simple Storage Service). After the AWS DMS S3 data migration starts, the complete deployment and tracking of infrastructure including error reporting, software patching, and replicating data changes that take place in the source database during migrationare handled by DMS.

Why do organizations today prefer to migrate databases with AWS DMS to S3?

·        First, and most importantly, you can choose the access levels as per need. It can either be high-price unlimited storage space or low-cost limited storage capabilities.

·        Further, with S3, billions of Batch Operations can be done.

·        Get all the attendant benefits of a cloud-based ecosystem like data security, cost efficiencies, and data replication.

·        By migrating databases using AWS DMS to S3, you get unmatched data scalability and durability. It leads to huge savings in costs as you can quickly download additional storage whenever there is a spike in demand by paying only for the resources used. There is no need to install hardware and software to meet this temporary surge in demand for storage space.

·        Data can be migrated from any database supported by S3 with AWS DMS which helps in the seamless migration of databases to the AWS. It is easy to replicate data continually and several databases can be consolidated into a single petabyte-sized one by streaming data to Amazon Redshift or Amazon S3.

·        Finally, you can carry out AWS DMS to S3from any database it supports. When Amazon S3 is used as a target in any AWS DMS activity, both the full load and CDC are written in a comma-separated value format by default.

However, before starting AWS DMS to S3, certain preconditions have to be followed. First, the S3 bucket used as the target has to be in the same AWS Region as the DMS replication instance that is used to migrate the data. Next, the AWS account used in AWS DMS to S3 must have an IAM role with write and delete access to the S3 bucket that is used as a target. Finally, DMS (dms.amazonaws.com) must be added as a trusted entity to the IAM role. However, ensure that the role assigned to the user account has the required permissions before setting up account access.

These are some of the ways to use Amazon S3 as a target for AWS data migration service.

[richardavisstuff]

Everything you need to know about data migration to AWS

No matter the industry, companies and organisations of all sizes are increasingly consuming and creating massive amounts of data. Further, the complexity of this data also increases with each update and change. This data needs to be stored, processed and updated constantly, which requires the right tools and solutions. To analyse data and derive valuable insights from it, businesses need to follow the right processes to make data work for them.

This is where tools like Amazon ETL come into the picture. To understand its role and impact in streamlining data collection, updation and utilisation, it is important to start with understanding what ETL stands for. Basically, ETL is a three step process which stands for Extract, Transform and Load, and it operates according to its name. Step one is where data is ‘extracted’ from databases or multiple data sources. Step two is where data is ‘transformed’ in different ways or converted into different file formats. Step three is where transformed data is ‘loaded’ into a data warehouse for further use and application.

Now that ETL is clear to you, let’s take a closer look at AWS as a cloud computing platform and data warehousing solution. In the AWS environment, there are many different data sources and warehouse options, including - AWS Redshift, AWS Simple Storage Service, AWS Relational Database Service and AWS Elastic Compute Cloud. Each service comes with its own set of features, capabilities and advantages.

ETL (Extract, Transform, Load) is a process of data replication from one source to a destination, and it requires complex coding and careful planning to ensure no data is lost, leaked or inaccurately transformed during the replication process. This is where tools come into the picture using which businesses can easily automate their ETL pipeline. With a cloud based tool, data administrators and engineers can easily define data pipelines and ensure data is extracted, transformed and loaded into a destination database, warehouse or lake.

If you are ready to leverage the power of Amazon’s cloud computing platform, the first step is to choose the right Amazon ETL tool to meet your data transformation and replication needs. If you are intimidated by the choices available or simply want to make an objective comparison between them, then keep in mind the following aspects while choosing an ETL tool:

a.    An ETL tool must have a wide range of data transformation capabilities to perform calculations and transformations from different sources.

b.    An ETL tool must install and integrate with your existing data architecture.  

c.    An ETL tool should allow real-time data streaming and replication.

Bryteflow is one tool which has all the above features and a lot more to meet your data transformation needs.