Apache Spark is one of the world’s most popular—and powerful—engines for processing big data analytics. But as with any big data platform, its efficiency depends on the infrastructure underneath it. And in fact, 85% of big data projects fail. Using a data lake is part of an effective strategy for mitigating many of the hurdles that impact big data projects (storage, retrieval, consolidation, etc.), but that alone can’t necessarily cover processing and resource issues. When projects call for many parallel Spark jobs, complex operations can compete for resources. The result is crashing jobs or insights that arrive too slowly to be effective.
Thus, getting data into a single-sourced repository like a data lake is only half the big data equation. If tools can’t finish their designated tasks due to resource issues, it defeats the purpose of ingesting all that data. To that end, it’s clear that handling tasks like bursty workloads and dealing with excessive unstructured data needs a platform to manage and support such things while getting the most out of Apache Spark.
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Managing Spark for big data projects comes with a number of inherent challenges. Each of these is unique and requires its own solution; the trick, then, is to address all of these in a unified way.
Challenge 1: Processing—Large amounts of data require large amounts of resources to effectively process and produce results. Juggling resource allocation and prioritization is a monumental task and can easily become a hindrance when not managed well. Striking a fine balance between processing and resources is the best way for Spark to maximize efficiency.
Challenge 2: Elasticity—True elasticity in data management is the goal of any infrastructure, enabling easy scalability and adaptability to maximize computing and financial resources while taking advantage of data lakes. In the cloud world, a pay-as-you-go model provides greater control over budgets, and this control ensures that resources aren’t wasted. However, it also introduces potential hurdles, and balancing those needs with elasticity requires an agile solution.
Challenge 3: Simplicity—Elegant development ensures a streamlined and simplified process, one that ensures projects don’t collapse under their own complications. Achieving this requires making the most out of the existing framework, including data lakes. That balance comes from seamless and simple integration, which means developing the project with an eye on both sides of this divide.
Challenge 4: Security—For all big data projects, security is one of the highest priorities and provides constant challenges. Between Spark preparation/management and inherent infrastructure design, security can be a moving target in balancing many requirements.
Oracle Cloud Infrastructure Data Flow was designed specifically to make Apache Spark projects easier to develop and run, both for users and from an infrastructure perspective. This opens the door to a wider range of big data and machine learning possibilities, while maximizing the capabilities of data lakes and data warehouses. By empowering Spark, projects can be widened in scope and executed more quickly, resulting in a wider range of insights.
Oracle Cloud Infrastructure Data Flow is a fully managed Spark service with near-zero administrative overhead. As an under-the-hood engine to power your Spark projects, this platform can import or run existing Spark apps from EMR, Databricks, or Hadoop, while maximizing and balancing resources in a highly secure environment. As a pay-as-you-go service that utilizes the Oracle Cloud Infrastructure framework, Oracle Cloud Infrastructure Data Flow delivers an elastic big data experience for broader possibilities and greater insights.
How does Oracle Cloud Infrastructure Data Flow do this? Let’s examine three specific use cases to get a clearer view.
Unstructured data makes up the bulk of today’s data. At the same time, capacity within a data warehouse is at a premium. Any unstructured datasets that live in this space are taking up resources and generally too large to query within the warehouse itself. For example, logs or sensor data are currently being loaded into a data warehouse or data mart before undergoing the extract/transform/load (ETL) process. This often requires computing summaries such as average, minimum, and maximum in the data warehouse, which comes with a high coast despite its simple workload.
Spark can deliver this for more economically. The data lives in object store, and Spark runs the job and summarizes. The results are then delivered to the data warehouse, optimizing cost and freeing up capacity. Oracle Cloud Infrastructure Data Flow can manage this overall process, balancing resources and overseeing Spark jobs.
Operational databases and data warehouses can only hold so much data. In many cases, both sources can be filled with necessary but seldom accessed data. What’s the solution? If you delete it, it must be re-ingested as needed. If you keep it there, it takes away valuable capacity from other more regularly used data. One way or another, this status quo creates a model that is unsustainable, either from a storage or a budget perspective. Oracle Cloud Infrastructure Data Flow powers an alternative, one that is simpler and easier for both sides of the equation.
Oracle Cloud Infrastructure Data Flow's output management capabilities optimizes the ability to handle queries done by Spark. In this example, data stored in object store (the archived part) will be queried using Spark (the active part). With Oracle Cloud Infrastructure Data Flow, job outputs are securely captured, stored, and made available, all with just one click or API call.
Provisioning fixed capacity can create inefficient budgets, but with bursty workloads, resource needs can spike at unexpected intervals. Some workloads only run once a day, once a week, or once a month. They can run at regular intervals or be occasionally used within that span. Such spikes are a drain on resources, leaving few options. One option is to absorb this spike, but it can slow down processing across the board, possibly even cause projects to fail. Another option is to increase budgets and purchase more computer power. Neither of these is ideal.
However, Oracle Cloud Infrastructure Data Flow provides a smarter and more efficient way for handling bursty workloads. With Oracle’s dynamic tool, resources can be automatically shifted to handle burst jobs without the need to plan for inefficient resource purchasing. By having powerful management of this process, overall costs are lowered, allowing IT staff to get a clearer view of usage and budget for future planning purposes.
Data lakes built with Spark can use Oracle Cloud Infrastructure Data Flow to get the most out of Spark applications—all with minimal management or impact on resources. This allows resources and staff to focus on application development rather than unnecessary logistics. The result? More possibilities, more time, and broader scopes, all leading to greater possibilities. Learn more about how Oracle Cloud Infrastructure Data Flow can transform the way your organization uses big data and a data lake—and don't forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.