When considering a data migration strategy, one critical decision that businesses face is whether to move all data to a Data Lake (DL). I’ve been a data engineer for nearly two decades, and throughout my career I’ve observed that migrating all data into a Data Lake in one monolithic project can present numerous challenges and costs.
Advantages of a Data Lake
A Data Lake can be advantageous if you're working with unstructured data or if you're trying to implement machine learning algorithms. A DL allows you to store massive amounts of diverse data types without the need for a strict schema, making it suitable for complex analysis and future-proofing your data architecture.
However, if your current analytics platform answers most of your questions, you might want to consider the hidden costs of a full migration to a Data Lake before making the leap. Let me share a few insights and experiences to help you make an informed decision.
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The Costs of Migrating All Data to a Data Lake
Cloud hosting costs can quickly escalate when migrating to a Data Lake, as it often requires large amounts of storage and processing power. As data volumes grow, so do storage and compute costs, particularly if data retrieval and processing are frequent. Additionally, the cost of data movement in and out of the cloud can add up. Monitoring usage closely is essential to avoid unexpected expenses and keep costs under control.
The transition to a DL can also divert your team’s attention from existing projects. Staff may need to focus on migration efforts, which can lead to delays or reduced quality in other ongoing projects. This shift can create a ripple effect, impacting business operations and timelines as teams adjust to the new demands.
...and allows you to learn from each phase, making adjustments and improvements along the way.
Moving all data to a DL at once can disrupt workflows for teams that rely on data, leading to frustration and interruptions. Data consumers accustomed to quick and seamless access may experience delays, which can dampen morale. A complete migration can also strain relationships between data teams and stakeholders, particularly if data is temporarily unavailable or results become inconsistent during the process. Additionally, engineers often thrive on achieving smaller milestones and seeing progress throughout a project. An all-at-once migration rarely provides these opportunities, which can lead to potential disengagement.
A Better Approach to Data Migration
I have never seen a shift to a Data Lake go completely smoothly, and I have always seen the users who need to consume the data lose faith at some point in the process. Instead of migrating all data to a DL at once, consider a phased approach.
Limit Migration Scope
Segment your data into domains based on downstream dependencies. This approach allows for a more targeted and strategic migration, enabling you to prioritize certain types of data or datasets with the highest impact. By focusing on specific areas at a time, you can more easily conduct thorough testing and quality assurance. This step-by-step strategy reduces risk and allows you to learn from each phase, making adjustments and improvements along the way.
Allocate Resources
Successfully managing the migration of a data domain while supporting existing solutions requires careful allocation of resources. Maintaining support for existing solutions is critical to minimize disruptions and keep operations running smoothly. Meanwhile, you'll need dedicated personnel working in parallel to oversee the migration process, ensuring it runs smoothly and efficiently.
Follow My Five-Step Plan
Repeat this plan for each data domain:
- Migrate a parallel data stream to populate the DL
- Create new tables in a Data Mart sourced from the DL that mirror the old tables' structure
- Redirect old artifacts to the new Data Mart and conduct thorough testing
- Freely reallocate resources previously supporting the old solution (maybe to DL support or migrations for other domains)
- Adjust business processes to route support for the artifacts to the new DL team
When considering the tradeoffs between mirroring the old table structure and rewriting reports based on new table structures during migration, it's crucial to weigh the benefits and drawbacks of each approach. While rewriting reports offers advantages like improved efficiency, we advocate for mirroring the old structure. Why? Because it simplifies creating apples-to-apples comparisons during testing. This ensures any issues can be attributed to the migration process rather than report differences, streamlining troubleshooting for a smoother transition.
Conclusion
Migrating data effectively requires careful planning and execution. By understanding the potential costs and choosing a strategic approach to migration, you can ensure a smoother transition to a Data Lake. If you need expert guidance in developing and implementing a cost-effective data migration strategy, feel free to contact Performance Automata for a free consultation. Let us help you navigate the journey to a Data Lake with confidence.