Embracing the Data Lake Architecture Starts with Deeply Getting to Know Your Workloads

An effective data lake architecture serves multitudes of different workloads off the same sources of truth, sharing both datasets and common business logic. Successful data lake deployments require minimal data duplication, offer strong discovery capabilities and fast delivery methods for new datasets and insights, as well as consistent business KPI definitions.

Varada’s dynamic and adaptive indexing solution enables to balance performance and cost with zero data-ops.

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Infrastructure Monitoring Doesn’t Cut It Anymore

Monitoring Doesn’t
Cut It Anymore

Effective data lake deployment requires one element that is often overlooked: deep understanding of the behavior and performance of each workload (set of queries that serves a business logic). Different workloads have different levels of impact on organizations’ business. Ranging from mission-critical, customer facing, internal dashboards, etc. Each workload, according to its priority, has its budget and the expected performance it requires.
Once organizations start to experiment with serving queries directly on the data lake, they are challenged with the transition from storage-only scaling to effectively serving critical workloads.
Most data platforms enable cluster monitoring of classic, technical metrics, that we know from the old “IT” world; metrics such as CPU load and RAM usage. This approach fails to align the business questions stream (workloads) and the TCO it generates.
Understanding the true cost of analytics requires expanding the classic “server monitoring approach” into a much deeper observability into the logic behind resource consumption, i.e. repeating query patterns, hidden dependencies, failure modes, cost and performance issues, etc.

Stay Focused on Meeting Business KPIs

Stay Focused on Meeting
Business KPIs

Varada delivers deep and actionable workload-level observability that supports this transition to understanding how data is used by different workloads, how resources are allocated among different workloads and users, how and why bottlenecks occur, etc.
The workload perspective enables data teams to focus engineering efforts on meeting business requirements. As infrastructure bills heavily weigh on the momentum of analytics projects, even to the point of negative profitability, Varada delivers a much deeper understanding that empowers data teams to move the knob of cost-performance, such as spend, ratio vis-a-vis the actual business value or importance of the workload at hand. Schedule A Demo

Observability Accelerates Data Lake Adoption

Increase user satisfaction and user experience with faster and predictable performance

Increase user satisfaction and user experience with faster and predictable performance

Workload-level observability empowers data teams to accurately evaluate whether the user experience meets requirements. Understanding workload bottlenecks allows data engineering efforts to focus on the most impactful actions and optimizations. Both the data and the nature of business user questions asked tend to change, sometimes abruptly. Without a deep monitoring, changes are often noticed and handled only after things break. By monitoring the workload behavior and identifying changes such as deteriorated execution times or inconsistent results, data teams can act preemptively and quickly adapt to changes.

Delicately balancing between performance and budget

Delicately balancing between performance and budget

The cost of data workloads is notoriously hard to predict and to keep under control. Workload-level observability enables identifying which query patterns consume resources ineffectively and drive the cost of a workload. Armed with this information, data teams can better predict the spend on a workload and focus their cost reduction efforts where they matter the most. Furthermore, workload-level observability enables data teams to prioritize workloads according to their business impact and focus their price-performance optimizations accordingly.

Faster delivery and higher quality insights

Faster delivery and higher quality insights

Many analytic processes are iterative by nature. With business objectives in mind, analysts map the realm of data to extract relevant insights, verify them and understand their context and impact. Being able to ask more questions, and being able to ask them interactively without interrupting the analysis flow, leads to better understanding of the data. Strong visibility of the workload enables data teams to accurately evaluate and optimize to deliver this sought-after user experience.

Workload-Level Observability is a Critical Data Management Tool

Varada Data Virtualization

Group queries intro workloads based on business function

Varada enables easy mapping of any query to a user-defined business workload. Data teams can monitor each workload to ensure performance requirements are met within the allocated budget. Workload-level observability enables data teams to gain control by effectively identifying and optimizing high priority workloads, instead of optimizing each queries one-by-one.

Get actionable insights based on workloads usage patterns

Varada automatically identifies hotspots and bottlenecks and enables data teams to promptly react by allocating resources in a single click.

Monitor and analyze workload behavior over time

Varada workload monitoring dashboard shows trends and detects changes across multiple dimensions, such as popularity of datasets, resource consumption of users, etc. By focusing on changes, data teams can proactively and effectively optimize.

Set materialization strategies that align with business requirements

Varada enables data teams to leverage different acceleration strategies, such as caching, multi-dimensional indexing, text indexing (based on Lucene), and more. Data teams can act on a highly granular resolution -- from a single partition on a single column up to entire datasets; complex rules can be set by UI or by API.

Cut down DataOps to support fast time to insights

Varada continuously monitors workload usage patterns and offers best-fit materialization strategies for each workload. Data teams can easily set priorities, performance requirements and budget caps, which enable Varada’s platform to automatically adapt to changes in workload behavior and resource consumption. Data teams can enjoy a true hands-free query acceleration which is designed to meet workload business requirements.
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