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Migrate to Google Cloud Databases

Database is one of the most important assets of an organisation and Cloud Platform helps an organisation to safeguard, process, manipulate, analyze, store and deliver the required business data as per convenience.

Migrate to Google Cloud Databases

As you know it well, a Database is a data structure or an organized collection of data, which can be easily accessed and managed. The data is organized into tables, rows, columns, and indexed to make it easier for data manipulation as well as retrieval.

Database is one of the most important assets of an organisation and Cloud Platform helps an organisation to safeguard, process, manipulate, analyze, store and deliver the required business data as per convenience.

Types of databases:

  1. Relational Database - Representation of Data in Tables and Rows e.g. MySQL, PostgreSQL, SQLite3.

  2. Non-relational Database - Document oriented Databases e.g. MongoDB in JSON format.

Google makes this manipulation and storage of data simple by introducing its DBMS (Database Management Systems) which includes both Relational and Non-relational usage.

When different apps are migrated from one platform to another, the database happens to be the most difficult aspect to migrate as-it-is without any data-loss and application logic manipulation.

Google Cloud Platform (GCP) solves this issue with migration assessment guide, advanced and efficient migration tools, collaboration with different partners to facilitate easy and safe migration without any data-loss.

You can just lift and shift your database to GCP with 100% open-source-compatible databases. Many customers leverage built-in features to minimize downtime during migration. Whether you are moving from proprietary to open-source databases or migrating from traditional databases to scalable cloud-native databases, you can make use of migration tools to make migration simple. GCP database migration partners provide tools like migration assessment that scans your database and provides a migration difficulty score based on feature and data type compatibility.

Some common Databases and Google’s solution to it:

GCP Product

Cloud SQL

Cloud Spanner

Cloud BigTable

Cloud DataStore

Cloud Storage

Legacy DB

MySQL, PostgreSQL, DynamoDB, ORACLE DB etc.

MySQL, PostgreSQL, DynamoDB, ORACLE DB

etc.

MongoDB,

HBase

etc.

MongoDB,

HBase

etc.

Media Files,

Docs, Objects

 

GCP Data Migration Product:

Cloud Data Transfer: Whether you have 50 GBs or 50 PB of data, or you have access to a T1-line or a 10 Gbps network connection, GCP offers solutions to meet your data transfer needs and get your data on the cloud quickly and securely.

Steps for Migration from On-premises to Cloud Platform: 

Step 1 - Assessment for Migration:

Before you start the pilot for migration, you need to take account of your applications and how suitable they are for the cloud. Points to consider include, but are not limited to, hardware and performance requisites, users, licensing, compliance requirements and application dependencies.

Step 2 - Migrate Existing Data: 

This is the pilot step wherein you create an initial copy of your existing workload data in a cloud data warehouse. This process will require choosing the right cloud data warehouse for your organization, after that you need to make a copy of your original data. There are two main challenges in this step. 

 The first is to experiment and choose the right infrastructure for your organization. To do this, you might have to select a smaller data set and migrate it to several different data warehouses for comparison. 

Step 3 - Set up Ongoing Replication:

After exporting the first snapshot of your on-prem data, and after copying it to your cloud data warehouse, the next step you need to do is set up an ongoing synchronization process. The replication process which is ongoing is more complicated than a single copy operation, as it is actually a series of real-time incremental copy operation. Each and every operation requires capturing changes to the data and its schema and applying those changes to the cloud data warehouse.

Step 4- Migrate your legacy data applications:


Using cloud BI tools, you can then prioritize migration of your custom reporting tools and data applications. But you will face a lot of  technical challenges as you might discover that ODBC drivers need to be replaced, and the queries need to be adjusted or rewritten. Changes in the data model may also be required, to fully utilize the performance advantages of your cloud data warehouse. Relying on a data-synchronization mechanism which can accommodate for transformations, will help the implementation of these data model changes dynamic and flexible. 

Step 5- Migrate your Legacy processes

Once all the legacy data applications have been migrated to the cloud, the final step is to point the processes to the cloud data warehouse. In a few cases, it will be just a change of configuration, and in others, it may require a complete re-configuration.


Step 6- Optimization and Performance Tuning:


You are now done with migration and you can start planning for optimization. Once an application and its data have been migrated to GCP, you can consider all the cool ways to make it better. You can add redundancy in the form of availability zones, elasticity with auto-scaling groups, or improved monitoring through Stackdriver. You might want to offload static assets from your application tier into Cloud Storage, or decouple tiers by using Pub/Sub. Google’s Deployment Manager can make it easier to launch and scale new instances, and replicating the configuration into a second region can protect your data and apps from a regional outage.


Congratulations your valuable data have been Migrated!



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