Avoiding these 10 pitfalls will ensure Hadoop data migration success and help avoid costly business risk along the way.
The expression “data is king” is common in today’s world of digital information, and for good reason. Data is the most important parameter for any company and must guide all decision making. The success an organization will experience in today’s age depends largely on how effectively they collect and use their data.
To do so, most organizations are modernizing their data architecture and moving their Hadoop deployments to the cloud, and those migrations oftentimes need to be simple and efficient. When it comes to migrating Hadoop data to the cloud, there are a number of best practices to help guide organizations along the journey.
However, where there are best practices, there can also be pitfalls to watch for and avoid. For example, consider the unnecessary business risks and IT costs that could occur if organizations mistakenly do the following 10 things as part of their Hadoop data migrations.
Top 10 Hadoop Data Migration Mistakes to Avoid
1) Assume that approaches that work at a small scale will work for big data – This trap can lead to significant challenges and inefficiencies. Small-scale solutions often lack the robustness, scalability, and performance optimization required for handling massive datasets. These approaches may struggle with processing speed, data integrity, and storage limitations when scaled up, leading to bottlenecks and system failures. Additionally, big data requires specialized tools and frameworks to manage distributed computing, parallel processing, and real-time analytics, which small-scale methods typically do not address. Thus, it is crucial to design and implement solutions specifically tailored for big data to ensure reliability, efficiency, and scalability.
2) Miss updates that are made to data at the source after your migration has begun – Don’t assume that your data remains unchanged during a migration project. If you intend to continue to operate your systems while migration is underway, you will need to account for how those systems create, modify, and delete data. Your migration approach needs a robust and understandable mechanism to ensure changes to your source datasets apply to your new environment.
3) Select a migration approach that requires data to be frozen at the source, resulting in business disruption – This method halts data updates and transactions, leading to downtime, operational delays, and potential revenue loss, affecting customer service and overall business continuity. Additionally, this migration approach can expose an organization to risks associated with their data integrity and data accuracy. It is essential for any organization to carefully assess and appropriately plan migration strategies to minimize any potential downtime.
4) Impact your business due to lack of bandwidth management capability – This trap can significantly hinder an organization, leading to decreased productivity and operational inefficiencies. Without proper bandwidth control, critical applications may experience slowdowns or outages, which would then affect communication, data transfers, and overall workflow throughout the organization. This can result in frustrated employees and customers, potential data loss, and increased downtime, all of which negatively impact business continuity and profitability. Additionally, unmanaged bandwidth can lead to higher operational costs as organizations may under-utilize their network resources, resulting in an organization losing its competitive edge and affecting its growth potential.
5) Require human interventions to recover from an outage when transfers fail or are interrupted – This can lead to significant delays and operational disruptions. Manual recovery is time-consuming and increases the risk of errors, prolonging downtime and affecting business continuity. It also necessitates having skilled personnel available at all times to diagnose issues and restore processes, which can be costly and inefficient. Automating recovery mechanisms and implementing robust error-handling protocols are essential to minimize these risks and ensure a smoother, more reliable transfer process.
6) Establish a unidirectional source/target architecture that is unable to scale beyond two endpoints – This approach can severely limit a system’s growth potential. This rigid structure constrains data flow to a single path, preventing the integration of additional data sources or destinations. As an organization’s data volume increases, this can become a potential bottleneck, unable to support the complexity and scalability required for data. This limitation can slow down data processing, analytics capabilities, and overall system performance.
7) Attempt to manually synchronize copies when data has been changed in more than one location – This process is full of challenges, risks, and lots of errors and can sometimes be very labor-intensive, leading to possible inconsistencies and corruption in the data. Additionally, as data volumes grow, the complexity and time required for manual synchronization increases, making it an unsustainable solution for maintaining data integrity and accuracy.
8) Program, maintain, and manage your own custom big data migration scripts – This approach requires more careful planning than organizations are typically capable of. Organizations should start by understanding both the source and target data structures and choose suitable programming languages and frameworks, like Python with Hadoop. An organization should also consider writing well-documented code with robust error handling and logging. Maintaining scripts by regularly testing with sample datasets and automating execution using tools and programs that are readily available. To be able to effectively manage big data, an organization should consider using cloud services and continuously monitor performance while making adjustments as needed to ensure reliability and efficiency.
9) Fail to account for application dependencies that cause data gravity – This trap not only causes data gravity but can sometimes create challenges in data management and processing. When application dependencies are not considered, data may become paired with specific environments or platforms, limiting flexibility and hindering scalability. This can lead to inefficiencies in data access, processing, and integration, ultimately impacting performance and agility. Properly assessing and managing application dependencies is crucial to avoid compounding data gravity pitfalls and ensure that data can be effectively utilized and migrated across different systems.
10) Establish an architecture that fails to keep your data secure while in transit – This trap exposes an organization to significant cybersecurity risks. Without proper encryption and secure transmission protocols, sensitive information becomes vulnerable to interception, which can compromise data and confidentiality. This oversight can lead to financial losses and damage to any organization’s reputation.
See also: Hadoop Data in the Dark? How Governance, Metadata Can Help
A Final Word About a Successful Data Migration
While data may be king in today’s business world, it doesn’t really produce value if it’s not properly planned for or managed. Stakes are always high with a Hadoop migration strategy, and the impact can have a significant impact on the bottom line of any organization. Organizations can no longer take the risk of sitting back and hoping for the best during a Hadoop migration. Avoiding these ten pitfalls will ensure migration success and help avoid costly business risk along the way.
