September 9, 2021 By Kevin Shen 2 min read

Put your focus back on analytics, get the insights to make your business successful and let IBM Analytics Engine manage all the infrastructure and cluster management details.

IBM Analytics Engine provides Hadoop and Spark environments that optimize the scaling of compute and storage by separating the two. The new Serverless Plan takes this optimization a step forward by offering a way for customers to get almost 100% utilization with Spark instances. To achieve this high utilization, Analytics Engine manages the provisioning and de-provisioning of Spark clusters to ensure resources are running when there is a submitted workload, saving time and costs.

Today, we’re seeing common cluster utilization for current Apache Spark users range from 20% to 60%, leaving a ton of compute resources on the table, sitting idle. Keeping these clusters provisioned and managed also takes up your team’s time and effort, with the potential for costly errors when estimating and sizing the appropriate resources for an analytics workload.

The new IBM Analytics Engine Serverless plan offers a consumption-based usage model to eliminate these challenges. Customers only pay for the compute resources they consume and only consume what they need.

IBM Analytics Engine Serverless helps make Spark easier to consume, and the new plan’s per-second billing helps users control costs. In addition to supporting the latest version of Apache Spark, Analytics Engine Serverless has persistent Spark customization within an instance, so users can bring their own libraries and override the default Spark configuration, with the ability to keep changes each time a new cluster is provisioned.

With add-ons built by IBM Research (such as geospatial toolkit, data skipping and Parquet modular encryption) and integration with IBM Cloud Object Storage, Watson Knowledge Catalog and Watson Studio, you can ramp up your analytics with fast cluster provisioning managed for you. As part of the IBM Cloud Pak® for Data, Analytics Engine works with the tools you already have to support every step in the data science value chain.

Get started

Only pay for what you use, manage less and put your focus back on the business and analytics.

Provision an IBM Analytics Engine Serverless plan instance here, and get $200 in IBM Cloud credit if this is your first time signing up.

Learn more about Analytics Engine here.

More from Analytics

How data stores and governance impact your AI initiatives

6 min read - Organizations with a firm grasp on how, where, and when to use artificial intelligence (AI) can take advantage of any number of AI-based capabilities such as: Content generation Task automation Code creation Large-scale classification Summarization of dense and/or complex documents Information extraction IT security optimization Be it healthcare, hospitality, finance, or manufacturing, the beneficial use cases of AI are virtually limitless in every industry. But the implementation of AI is only one piece of the puzzle. The tasks behind efficient,…

IBM and ESPN use AI models built with watsonx to transform fantasy football data into insight

4 min read - If you play fantasy football, you are no stranger to data-driven decision-making. Every week during football season, an estimated 60 million Americans pore over player statistics, point projections and trade proposals, looking for those elusive insights to guide their roster decisions and lead them to victory. But numbers only tell half the story. For the past seven years, ESPN has worked closely with IBM to help tell the whole tale. And this year, ESPN Fantasy Football is using AI models…

Data science vs data analytics: Unpacking the differences

5 min read - Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to…

Financial planning & budgeting: Navigating the Budgeting Paradox

5 min read - Budgeting, an essential pillar of financial planning for organizations, often presents a unique dilemma known as the “Budgeting Paradox.” Ideally, a budget should give the most accurate and timely idea of anticipated revenues and expenses. However, the traditional budgeting process, in its pursuit of precision and consensus, can take several months. By the time the budget is finalized and approved, it might already be outdated.In today's rapid pace of change and unpredictability, the conventional budgeting process is coming under scrutiny.It's…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters