Why do we need a modernized data warehouse? It has been noted that a time-consuming development process and limited support of Self-service BI are significant reasons behind migrating to cloud-based data warehouses from the legacy system. Most of the tech professionals find that the legacy data warehouse development process lacks agility and reliability.
Data warehouse modernization is the first step towards the digitalization of your organization. A data warehouse is a process of siloed data migrations to cloud-based storage from legacy systems. It boosts the organization’s agility and productivity, eliminating inefficiencies and complexities of the legacy systems. Modern data warehouse enables organizations to have real-time analytics, the self-service discovery of insights, and faster ingestion. Modernizing a Data warehouse is a necessity, but it is not easy. There are challenges in the process that need to be addressed. Let’s discuss significant challenges and benefits associated with the data warehouse modernization process.
What considerations should one have in mind while modernizing the data warehouse?
- Would there be any disturbance in our day-to-day operations due to the data modernization process?
- Do we need a team of skilled engineers to modernize the data warehouse?
- Can we forecast the expenditure? What about our current investment in existing data infrastructure?
- Should we migrate our workload as it is, or do we need to re-engineer from scratch?
The considerations mentioned above for organizations are crucial to ensure smooth operations. Every organization should use the right approach to ensure there is no bottleneck in the smooth operation.
Whether you are new or have been using legacy systems, migrating to a new data warehouse involves challenges. Let’s discuss what these challenges are.
Managing Native Data Warehouse Assets
Organizations need to reconfigure the data models to reduce time-to-build duration and costs. Mapping existing legacy systems’ entity relationships, data types, partitioning strategies, indexing, and strategies with target schema is the biggest challenge for the organization. It means you cannot simply transform the schema by configuring the migration pipeline. However, the best strategy is cloud-native implementation which eliminates errors.
The two biggest roadblocks are mapping data and column types. Mapping data is a complex task. For example, AWS Redshift deals with the most common types of data sets because it is PostgreSQL-compatible; however, Google uses STRINGS for BigQuery instead of VARCHAR and puts REPEATED array types. In the same way, other technology vendors have different data mapping. Column type mapping also requires extensive efforts. For instance, Amazon Redshift does not support LOB types of Data, while Teradata supports BLOB and CLOB data types.
Auto-transforming Code
Manual or semi-automated migration is risky, but fully automated migration could also be risky without adding appropriate tools. Here are the reasons why this could be the problem:
- Lack of proper information about code logic
- Having problems in migrating complex scripts
- Incomplete availability of the required documentation
It is essential to convert ETL logic, including event-based error handling, writing, data cleansing, and reloading the processed data back to the data warehouse to ensure no error in transforming the existing code.
Lack of Agility in the Infrastructure
Analyzing Application Validation and Performance
Managing Technical Debt
If there is continuous procrastination of the effective data warehouse designs and defects in coding, it leads to a huge technical debt on the organization. Due to the longer period of these defects stacked in the systems, they create operational problems. Data modernization is the best way to eliminate technical debt and avoid further. However, identifying the interdependent workloads at the data level becomes essential to avoid technical debt. One of the common roadblocks for engineers is identifying technical debt at different levels in the organization. Determining an effective partitioning strategy helps in determining and handling the technical debt. Here are some of the common partitioning strategies:
- ‘Cluster by’ strategy
- Splitting strategy
- Number of buckets
- Other strategies include sorting by columns, distributing by keys, etc.