There are numerous advantages of using cloud data warehouse solutions, but at the same time, there are many challenges that businesses face while modernizing their data lakes or warehouses.
There are a few difficulties to consider while modernizing data platforms that may propel organizations to reconsider their conventional data management strategy and choose the appropriate data intelligence solutions for advanced analytics.
Let’s look at some of the most significant issues associated with data warehouse modernization.
During data migration, errors might be induced in the mapping and transformation logic. Data can be left in an inconsistent state due to runtime failures such as network outages or failed transactions. As a result, maintaining data lineage from source to target and the need for a data integration tool is critical.
Things to keep in mind while modernizing or migrating the traditional data warehouses are:
Ensure that workloads are appropriate for migration and that the migration has unambiguous, quantifiable goals, such as increased scalability, cheaper costs, or enhanced performance.
Many workloads may not be suitable for migrating to the cloud. Therefore, you must consider all aspects of the execution environment and ensure that great levels of capacity, performance, utilization, security, and availability can be achieved. If there’s something amiss, the workload should ideally be left on-premises.
Data quality issues are amplified when migration happens from a legacy system (with poor data quality) to a newer application that has a set of rich features and a strict data model. This necessitates a lot of preparation before the migration process actually initiates. Bad quality of data imposes considerable expenses for post-migration maintenance; with issues ranging from inadequate business intelligence to delay or disruption in business processes.
We also need to keep a track of each process within the data lineage system where we are conducting some data transformation or processing while constructing it. When the data asset is moving through any procedures, we need to map data pieces at every stage. As a result, tables, views, columns, and reports in databases, as well as ETL operations, must be tracked.
Therefore, to achieve data lineage metadata collection should be done after each data transformation cycle, which can be used for lineage representation.
For the past few years, privacy laws and regulations have been a subject of discussion in the digital industry. Today's businesses must adhere to stringent government regulations that affect everything from how customer data is handled to where it is housed.
Modern data warehouses, fortunately for many, address these concerns by introducing an abstraction layer that serves as a barrier between source systems and end-users, allowing businesses to design multiple data marts that deliver specific data based on requirements, and ensuring that regulatory requirements are met during the reporting process. Thus, choosing the ideal data intelligence solutions which fit into your ecosystem is paramount.
When working with data warehouses, defining an access control framework is critical. In the vast majority of situations, organizations are unable to distinguish between which departments or employees must have absolute access to the data warehouse.
Unnecessary strain on the system is created by not balancing resources and giving permissions efficiently, resulting in bottlenecks that might have been avoided. Unauthorized users may get access to critical source systems due to a lack of a suitable access control framework, which might be costly to the company.
When constructing or upgrading a data warehouse, it's critical to have a well-thought-out data strategy. Without a data strategy, not only would it be difficult for various teams to adapt to the new data warehouse, but it will also be impossible to realize all of the benefits that a data warehouse can provide.
This necessitates collaboration among stakeholders, which is why development, design, and planning must all be part of a single continuous process.
Importing data from a variety of sources into your data warehouse is critical for obtaining a comprehensive perspective of your business operations & procedures. As a result, all major modern data management and warehousing systems must be able to integrate with popular cloud platforms, apps, and databases including AWS Redshift, Oracle, and Microsoft Azure. Datablaze Software breathes a new life into your data management platform through multi-cloud and knowledge-graph-based analytics.
