Why CIOs, CDOs and data teams of organizations are considering migrating from Hadoop to a more modernized data architecture? A few reasons:
- Most organizations today are dealing with terabytes and petabytes of data. Hadoop wasn’t built to handle the kind of workloads that companies experience today.
- This phenomenal data growth needs advanced analytics to harness its full potential and Hadoop struggles to support such advanced data analytics and AI/ML. It can be a challenge to enable governed self-service analytics.
- Building AI models, whether for real-time or batch ingestion, necessitates the integration of multiple components.
- Hadoop is a resource and maintenance-intensive platform requiring 24×7 management and operation support by a highly skilled workforce.
- The complexities of the Hadoop architecture fail to allow data teams to free themselves from managing the infrastructure and focus on building new use cases.
- The financial implications of running and scaling Hadoop and costly license renewals push companies to make the shift to the more cost-effective cloud alternative.
Pitfalls to be Mindful of When Migrating Off from Hadoop
- Lack of proper planning and assessment of the existing Hadoop ecosystem and data leads to unexpected challenges.
- Misjudging data-related complexities such as nested structures or unstructured data leads to data transfer issues and data integrity concerns.
- Inadequate testing of the new architecture leads to performance issues and loss of data processing capabilities.
- A dearth of the right talent to execute the migration and support impacts continuous performance.
- Unrealistic migration timelines and rushed processes that result in lapses.
- Budget overruns due to inaccurate cost estimation of storage, processing, and data egress charges.
Hadoop Migration Best Practices
- Choosing the destination platform (e.g., Snowflake, Databricks, Azure Synapse Analytics, AWS EMR, and GCP BigQuery) based on what each platform has to offer in line with the organization’s specific needs and goals.
- Evaluating the budget and cost of the migration.
- Defining the migration strategy, whether it will be a gradual transition, a lift and shift, or a hybrid approach, depending on the existing infrastructure and data requirements.
- Identifying the technical steps and processes required for data transfer, validation, and testing.
- Determining the key stakeholders and resources at every stage of the migration process.
- Processes to ensure data governance during migration to preserve data quality and compliance.
- Security measures to safeguard data during and after migration and adhere to compliance standards.
- Optimizing data storage, processing, and performance in the new environment.
Ready to Migrate from Hadoop to a Modern Data Architecture?