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Databricks offers a complete platform for data and AI. Many companies adopt it to unify their data and analytics work. Yet, the path from implementation to getting its full value often has unexpected problems. These issues can slow progress and make you question the entire investment.
Is Your Databricks Investment Producing the Expected Results?
Many organizations find that a full return on investment (ROI) from their Databricks platform is hard to achieve. The most frequent challenges come from rising costs, complex implementation delays, internal skill gaps, and inconsistent data governance. Specialized consultants provide a clear plan to fix these issues and align the platform with your data strategy.
The gap between the platform’s potential and its actual performance is common. The complexity of the data lakehouse environment can make it difficult to get the desired operational outcomes. Without a clear, expert-guided approach, companies struggle to move past technical problems and generate real value from their data.
Challenge 1: How Do You Contain Unpredictable Databricks Costs?
You contain unpredictable Databricks costs through disciplined resource monitoring, ongoing cluster optimization, and adopting FinOps best practices. Costs often spiral because of idle clusters that run unnecessarily, inefficient code that consumes too much power, and poor storage choices.
A consultant helps by analyzing your usage, implementing cost controls, and giving you visibility into your spending. This approach lowers your Total Cost of Ownership (TCO).
Why Costs Escalate Beyond Forecasts
Unexpected bills from Azure/AWS cost management reports are often tied to specific technical issues. Your team might not have the time or specialized skills to find and fix them.
Common reasons for high costs include:
- Over-provisioned resources: Teams select larger, more expensive clusters than needed for their tasks, leading to wasted resource allocation.
- Inefficient Spark jobs: Poorly written code can run for hours instead of minutes. This consumes excessive compute resources.
- Long-running idle clusters: Clusters are often left running after a job is complete. This means you are paying for resources that are not doing any work.
Challenge 2: Are You Facing Problems with Databricks Implementation and Migration?
A successful implementation and migration require detailed planning, a solid architectural design, and a phased approach guided by experts. Without this structure, companies face major Databricks migration challenges. A flawed migration leads to project delays, data integrity problems, and significant budget overruns. This is a common issue when moving from legacy systems.
Common Migration Pitfalls to Avoid
Moving to Databricks involves more than just lifting and shifting data. Many teams underestimate the complexity of a data warehouse modernization project. A key difficulty is translating years of old code and proprietary SQL from systems like Hadoop or Teradata.
Another challenge is maintaining data consistency and quality during the move. If done poorly, it can disrupt daily operations that depend on that data.
How a Partner De-risks Your Project
A consulting partner uses a proven implementation plan to manage the complexity. They guide you through a structured process that reduces risk and gets you to your goals faster. This method creates a modern data architecture built for your needs.
The process typically includes these four phases:
- Assessment & Strategy: Understanding your current systems and defining clear goals.
- Architectural Design: Creating a detailed blueprint for your new environment.
- Phased Migration: Moving data, code, and users in manageable stages to limit disruption.
- Validation & Handover: Testing the new system completely and training your team to manage it.
Move from Databricks Complexity to ROI
We pinpoint the root causes of high costs, slow performance, and governance gaps in your Databricks platform.
Receive a clear roadmap to optimize your data platform.
Receive a clear roadmap to optimize your data platform.
Challenge 3: Does Your Team Have the Specialized Skills to Fully Utilize Databricks?
Most in-house teams lack the deep, specialized expertise needed to fully utilize Databricks. Key areas like Apache Spark optimization, Delta Lake architecture, and advanced security are often outside their experience. This skill gap leads directly to poor platform performance, security risks, and a failure to adopt valuable features that drive better results. Good data engineering requires a specific set of abilities.
The Specific Expertise Required
High-value Databricks work depends on skills that are hard to hire for. A Databricks expert brings knowledge that generalist data teams simply do not have.
This specialized expertise includes:
- Advanced Spark Tuning: Optimizing jobs to run faster and use fewer resources, directly cutting costs.
- Unity Catalog Governance: Setting up a secure and searchable catalog for all your data assets.
- Implementing CI/CD for Notebooks: Automating the testing and deployment of code to improve reliability.
- Optimizing the Photon Engine: Using Databricks’ native query engine for the fastest possible performance.
Consultants as Team Augmentation & Mentors
A consultant is more than just temporary help. Through team augmentation, an expert works alongside your employees on your actual projects. This process allows for direct knowledge transfer.
Your team learns by doing, guided by a mentor who can establish best practices for coding, MLOps, and platform management. They leave your team more capable than they found it.
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Challenge 4: How Do You Establish Reliable Data Governance and Security?
You establish reliable data governance by implementing a centralized framework with a tool like Unity Catalog. This must be combined with well-defined processes for data quality and access control.
Without this structure, your data lakehouse can become a “data swamp.” This exposes your organization to compliance risks like GDPR violations and erodes trust in the data used for reporting.
The Risks of a Poorly Governed Lakehouse
When governance is weak, operational problems appear quickly. Teams work with duplicate or outdated information. A lack of clear data lineage makes it almost impossible to trace errors back to their source. Inconsistent security rules create vulnerabilities. This prevents you from confidently connecting data to tools like Power BI for wider company use.
Building a Governance Framework with Unity Catalog
Consultants help design and build a governance model that fits your specific needs. They use Unity Catalog to create a single, organized place for all your data.
The process involves setting up fine-grained access policies so people only see the data they are supposed to. They also help define automated data quality rules. This work makes your data assets easy to find, trust, and use for analysis.
Your Roadmap to Databricks Success Starts with the Right Partner
Databricks challenges are complex and often connected. But they are solvable with strategic, expert guidance. A consulting partner like Multishoring provides more than just technical fixes. We deliver a strategic roadmap to align the platform with your company objectives. This turns your Databricks investment into a true asset.
Don’t let implementation problems and rising costs undermine your data strategy. Schedule a data & analytics maturity assessment with our Databricks experts today and discover your clear path to maximizing ROI.
