Seamless Data Migration to Databricks: A Complete Guide – Addepto

In today’s fast-paced business environment, data has become increasingly valuable to organizations than ever before. Business executives cannot afford to make crucial decisions based on instinct or guesswork. They need to be able to analyze and interpret available data and use it to make more informed decisions, improve operations and even connect with customers. [1]

According to a recent report by McKinsey Global Institute, organizations that rely on data to make crucial decisions are 23 times more likely to get new customers, six times more likely to retain customers, and 19 times more likely to become profitable. [2]

As data continues to drive many modern businesses, it has become increasingly important to establish effective and seamless data integration and data migration processes. Whether data is migrating from inputs to a data lake or from one centralized repository to another, a well-thought-out data migration plan is necessary. Without such a plan, organizations will end up with budget overruns and subpar data operations.

This post will explore what data migration is, how to plan Databricks migration, and a step-by-step guide on how to execute a successful migration process.

Understanding data migration

Data migration basically refers to the process of transferring existing data between data storage systems, file formats, databases, data centers, applications, or computer systems. This process usually involves extensive data preparation, extraction, and transformation to ensure success.
In most cases, data migration occurs when an organization introduces new systems and processes. The process is only considered complete when the old data storage system, database, data center, or computer system is shut down.

There are various reasons why an organization may choose to carry out data migration. They include:

• To replace, upgrade and expand existing data storage systems and equipment
• To establish a new data warehouse
• To replace or upgrade legacy software
• For website consolidation
• As part of infrastructure maintenance
• As part of the IT infrastructure move to the cloud
• To shift a centralized database to eliminate data silos and attain interoperability
• As part of data center relocation
• As part of the installation of new data storage and computer systems that will augment existing systems or applications

Read more about Mastering Databricks Deployment: A Step-by-Step Guide

Types of data migration

The following are the different types of data migration that exist today:

Application migration

Application migration involves the movement of data from one computing environment to another. It usually occurs when an organization changes application software or application vendors. The biggest challenge with this type of data migration is that old and new IT infrastructures may have different data models and work with different data formats.

Data center migration

A data center basically refers to a building or dedicated space that an organization uses to house its computer systems, critical applications, and related components. [3] Data center migration entails the movement of data center infrastructure from one location to another or the transfer of data from old data center systems to new ones at the same location.

Database migration

A database is a collection of structured data stored in a computer system. That said, database migration involves the movement of data from one Database Management System (DBMS) to another or upgrading an old version of a DBMS to the latest version. The former case is a bit more challenging than the latter, especially if the source and target databases use different data structures.

Cloud migration

Cloud migration is the movement of data from your company’s own IT environment to the cloud. This makes cloud migration a unique type of storage migration. Thanks to the recent shift to remote working due to the COVID-19 pandemic, many organizations have embraced cloud migration in an attempt to reduce IT infrastructure costs, improve cybersecurity, and gain a competitive advantage.

According to Precedence Research, the global cloud services market size is projected to hit around $1.63 trillion by 2030, growing at a staggering CAGR of 17.32% from 2022 to 2030. [4]

Business process migration

Business process migration is usually caused by mergers, acquisitions, business optimization, and reorganization in an attempt to address various competitive challenges or enter a new market. It involves the movement of business data and applications on customers, products, and business processes to a new IT environment.

Planning your migration

Databricks migration is a complex process that doesn’t have room for mistakes. Transferring sensitive data to Databricks Lakehouse is enough to put all stakeholders in an organization on edge. Therefore, before you gather the necessary requirements and embark on your migration journey, careful planning is a must. Having a solid Databricks migration plan will go a long way in ensuring minimal disruption and downtime to business operations.

Planning your migration should involve the following steps:

Know your data

A strategic migration plan should start with evaluating the data you have. Remember, the process you’ll use during the migration process will mainly depend on your data’s type, volume, and format. Therefore, your source data needs to undergo a complete audit to find out its volume, diversity, and overall quality.

It’s only after carrying out a complete audit of the data that you’ll know how it must be transformed, consolidated, and processed before transferring it to Databricks. Skipping this step could end up causing unexpected issues during the actual migration process.

Identify the systems that will be impacted

After auditing your data, the next step is to determine the specific systems that the Databricks migration process will impact. It’s very rare for a migration project to only impact the source and destination of the process. Most of the time, there are several systems that rely on the data being migrated to Databricks. Failure to identify these systems in good time will likely result in budget overruns and even project delays.

Project initiation

You need to identify the various stakeholders in the migration project and find out their areas of expertise. Once you identify stakeholders with the relevant expertise, brief them about the project and assign responsibilities. You also need to agree with these stakeholders on the communication channels to use during the migration project.

Data backup

This step involves backing up all the data you’ll use for the Databricks migration project to protect it against any failure that may lead to data loss. This way, you’ll be able to recover and restore your data in case something goes wrong during the process. [5]

Build and test your migration strategy

Once your data is fully audited and backed up, it’s time to create your Databricks migration strategy. This process may also involve pre-validation testing to ensure all systems function properly.

To build the ideal migration strategy, you can choose to recreate the schema with your source data and adjust it to suit the schema. You can also automate a big part of the process using a data integration tool used to automate multi-table updates.

After designing your migration strategy, proceed to test it in a sandbox environment. At this stage, consider bringing in an HTML developer, a data engineer, a system analyst, and a business analyst to help you design the best migration strategy possible.

Set budget and realistic timelines

After all the systems have been evaluated and the Databricks migration process has been built and tested, it becomes much easier to estimate the budget of the entire project and set schedules. A Databricks migration project can take a few minutes or hours, depending on the volume of the data and the difference between the source schema and the corresponding schema in Databricks.

Execute and validate

At this stage, it’s time to initiate and roll out the Databricks migration process. The extraction, transformation, and loading processes also take place at this stage. Once the process goes live, ensure you monitor and validate it to verify whether there is any sign of failure or downtime. Continuous communication with relevant stakeholders and business units is also vital during this process.

In the end, the process should be executed as per the set schedules and deadlines. You also want to ensure the data transferred to Databricks is complete and suitable for business use.

Decommission and monitoring

Once the migration process is complete, ensure you shut down and dispose of the old systems.

Executing the migration process

Seamless Databricks migration involves moving your data from other sources to Databricks while ensuring data integrity throughout the process. Transferring data from other sources to the Databricks platform offers you several benefits, including data pipeline orchestration, enhanced processing, reduced costs, collaboration, and data sharing capabilities, improved security, real-time analysis of streaming data, and scalability.

Here are the steps to follow to achieve a seamless and successful data migration to Databricks:

• Data Extraction: When extracting data from sources, use the appropriate methods to ensure data integrity and consistency.
• Inspect Your Data: Before migration, you should inspect your data to understand its source, format, diversity, quality, and volume.
• Choose Your Preferred Migration Tool: There are various ways to migrate data to Databricks including through Databricks Import/Export, Databricks utilities, Databricks command-line interface (CLI), and even third-party ETL tools.
• Data Transformation and Cleansing: Ensure you clean, transform, and process your data to align it with the schema and requirements of Databricks. At this stage, you may need to carry out data aggregation, merging, conversion, or even filtering to meet these requirements.
• Data Loading into Databricks: After transforming and cleaning your data, proceed to load it into Databricks using any of the suitable mechanisms, such as Databricks Lake, Databricks File System (DBFS), or even Databricks Spark connectors
• Testing and Validation: Once you’ve loaded your data into Databricks, validate it to ensure correctness. You may even compare the migrated data in Databricks with the source data to identify any inconsistencies or data integrity issues.
• Code Migration and Integration: Transfer any custom code, queries, or scripts from your source to Databricks. It’s vital to ensure the migrated code functions properly in the Databricks workspace.
• Monitoring and Optimization: Monitor your Databricks clusters for any performance issues. Use Databricks’ scalability and processing capabilities to optimize the performance of the migrated data and code.
• Post-Migration Tasks: Monitor the system and address any performance issues that may arise after the migration process. Consider providing training to everyone who will be working with the migrated data and code in Databricks.

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Final thoughts

Achieving a seamless and successful data migration to Databricks requires careful planning and execution. By following the above steps, you can move your important data to a more scalable and secure environment. However, you should know that such a migration is not a one-size-fits-all process.

So before embarking on this journey, consider your organization’s needs, data complexities, and objectives. This way, you’ll be able to execute this process in a way that benefits your organization in the long run.

To simplify your Databricks migration journey and ensure a smooth transition, consider the support of Databricks Deployment Services. Our expert team can streamline the migration process, optimize your data workflows, and empower your organization to harness the full potential of Databricks for your data-driven goals.

References

[1] GRow.com. Why is Data Important for Business. URL: https://www.grow.com/blog/data-important-business. Accessed August 17, 2023
[2] Mckinsey.com. How Customer Analytics Boosts Corporate Performance. URL: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance. Accessed August 17, 2023
[3] Ibm.com. What is a Data Center. URL: https://www.ibm.com/topics/data-centers. Accessed August 17, 2023
[4] Precedenceresearch.com. Cloud Services Market. URL: https://www.precedenceresearch.com/cloud-services-market. Accessed August 17, 2023
[5] Fluentpro.com. Top 7 Advantages of Data Backup and Recovery. URL: https://fluentpro.com/blog/top-7-advantages-of-data-backup-and-recovery/. Accessed August 17, 2023