Data Migration To The Cloud – The most forward-looking companies are adopting cloud solutions and opting for cloud data migration for data science and business intelligence as it provides a significant advantage over the competition. Also, the risks associated with data transfer to the cloud are now reduced to a minimum.
By Tetiana Boichenko • Sep 06, 2017 • 3 min read Successful cloud data migration for your business: 3 best practices
Data Migration To The Cloud
The most forward-looking companies are adopting cloud solutions and opting for cloud data migration for data science and business intelligence as it provides a significant advantage over the competition. Also, the risks associated with data transfer to the cloud are now reduced to a minimum.
What Is Lift And Shift Cloud Migration? 3 Main Cloud Migration Strategies
Depending on specific business needs, organizations choose to move to a private, public or hybrid cloud. Most companies choose public clouds because they offer benefits such as scalability, cost-effectiveness and reliability while paying. However, if organizations are concerned about moving critical data to the cloud, a hybrid cloud is the perfect solution, where the most critical data is hosted on-premise to avoid potential security risks, while the rest of the data is moved. Public cloud.
There are many public cloud vendors like AWS, Azure, Google Cloud, IBM etc. According to the 2017 State of the Cloud survey, AWS continues to be the leading cloud service provider (57 percent of respondents run applications on AWS).
Many companies choose cloud data migration with AWS for data science and BI or implement a hybrid solution because it offers advantages such as scalability, cost efficiency and reliability. Depending on your needs, you can choose lift and shift training, partial rewrite or full rewrite. The main challenge is to find the required expertise and experts in Big Data stack, understanding Python, Scala, SQL, business models of traditional systems and open source technologies. Data scientists and BI professionals have strong experience in migrating data warehouses to the cloud. Reach out to us for any related queries!
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A Complete Guide To Cloud Data Migration For Businesses
Moving a data warehouse from a legacy environment requires a significant initial investment in resources and time. There are many things to consider before and after the migration. You may need to redesign your data model, use a separate platform to handle changes to task scheduling and the application database driver. Therefore, companies need to take a strategic approach to streamlining the process. This article presents a step-by-step approach to migrating a data warehouse to the cloud.
A data warehouse is any system that collects data from multiple sources. A data warehouse is used as a centralized data repository for reporting and analysis purposes. Enterprise Data Warehouse (EDW) manages and stores business data. This data typically comes from various systems such as customer relationship management (CRM), enterprise resource planning (ERP), and physical records.
An on-premises data warehouse collects, stores, and analyzes data on on-site servers. As a result, organizations must manage hardware infrastructure. However, on-premise management is not always a viable option. Over the past few years, companies have started moving their data warehouses to the cloud. Here’s why:
Migrating data from an on-premises warehouse to a cloud-based environment creates many challenges. You may need to redesign your data model, use a separate platform for task scheduling, and integrate custom data applications.
Data Migration: Process, Strategy, Types, And Key Steps
Cloud data warehouses support different schemas and data types. AWS Redshift has the most common data types because it is compatible with PostgreSQL. Google BigQuery, on the other hand, uses STRING instead of VARCHAR and uses the REPEATED and RECORD array types of semi-structured objects. For semi-structured data, Snowflake supports OBJECT, VARIANT and ARRAY.
In addition, cloud data warehouses promote schema denormalization approaches for performance improvement. The increased storage required to store redundant data is relatively inexpensive. However, running JOINS on tables stored on a distributed server is very expensive and does not lead to the desired performance improvement. Therefore, you need to ensure that the data models in the cloud and on-premises are synchronized during the migration.
The stored procedure layer acts as a compact data application repository. It can be used to store large amounts of data and retain specialized knowledge. The ability to write and use stored procedures is often overlooked and overlooked by cloud data warehouses.
Cloud data warehouses like BigQuery, Snowflake support user-defined functions, but this is not enough. A common alternative is to use a separate platform to plan the orchestration of parameterized tasks or queries. There are open source options like Airflow and Luigi, as well as commercial cloud-based alternatives.
How To Save Time And Money Migrating Data To The Cloud
Database drivers such as ODBC/JDBC are used to connect your application to your data warehouse. These drivers usually behave differently. As a result, changing the application’s database driver requires various query modifications.
Some changes are obvious because SQL statements can cause visible errors. Other changes are less likely as different ODBC drivers may cause some data changes. For example, changes in time zone or time stamp format. These changes appear only as data conflicts. A more rigorous test is needed to detect it. As long as knowledge of these changes is shared, the entire organization can deal with them quickly.
Many companies think cloud migration is a one-time journey. However, in reality, the process of transferring data to the cloud should be gradual.
First, you need to create an initial copy of your data warehouse in the cloud. This process requires you to select the right cloud data warehouse for your needs and create an initial copy of all your data.
Assess And Migrate Apps With The Cloud Migration Assistant
You should also confirm the schema and format of the data you want to transfer to the cloud. Then, you need to transfer your schema to the cloud data warehouse before loading it. Finally, you should mark the time of the exported snapshot and use it when setting up a continuous replication process.
The next step is to set up a continuous synchronization process. Any synchronization process should be tested for reliability and latency. These parameters are critical to the success of an organization’s cloud migration strategy. You can create this synchronization yourself or use data pipeline services such as schemas and managing continuous replication of data. Once synchronization is ensured, you can begin migrating your entire infrastructure one component at a time.
After the migration pipeline is set up, you need to start migrating the business intelligence (BI) and analytics infrastructure. Analytics components are typically not central to an organization’s data infrastructure. Therefore, it makes a less risky migration destination. Examples of BI tools include Looker, W, Periscope, and Chartio.
Migration of legacy data applications introduces more technical challenges. You need to change the ODBC driver and fix or rewrite the query. To take full advantage of the performance benefits of your cloud data warehouse, you may also need to change the data model.
Upgrading From Oracle 11g To Cloud: Avoid The Pitfalls, Maximise The Benefits.
The final step is to recreate the transformation that creates your final data model in your new cloud environment. Although the initial investment in time, labor and money is high, adopting an ELT paradigm is much better in the long run than ETL. Tools like dbt allow you to write a SQL-based transformation layer on top of your data warehouse.
A typical data warehouse contains large volumes of data spanning multiple business areas. Moving all data at once guarantees failure. Especially when making significant design changes, you need to take extra steps to successfully move your data warehouse to the cloud.
An incremental approach allows you to continue to operate your on-premises data warehouse while your cloud data warehouse is available online. During this transition phase, tools such as synchronizing data between the old on-premises data warehouse and the new one in the cloud can be used. On-premise migration to the cloud is a necessary first step to cloud adoption, providing a fast track to data infrastructure modernization, innovation and the ability to rapidly transform business operations. But many companies still limit their use of the cloud to non-critical projects rather than critical functions.
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