Building algorithms is not easy. The in-demand data scientists who are tasked with that work need to streamline algorithm development. They require tools that reduce the need for complex coding and deep Apache Hadoop expertise.
How can you create the valuable insights that are the currency for the new economy while controlling complexity? Apache Spark might be the answer.
Apache Spark is an open source engine built specifically for data science. It helps simplify algorithm development and accelerate analytics results. With Spark, you can better extract value from big data, conducting deeper analyses and delivering results faster, all while reducing the time and effort required for coding.
Spark enables you to:
- Extend Hadoop. Spark complements Hadoop. While Hadoop is designed to manage big data, Spark is designed for analytics. Integrating Hadoop and Spark solutions enables you to generate new insights from big data.
- Streamline development. Developers and data scientists can use their existing expertise with programming languages such as Scala, Python and SQL to speed time to market. With Spark, there’s no need to learn new languages.
- Simplify data access. Spark removes data access complexity, providing seamless access to enterprise data with familiar tools. Built-in machine learning and graph algorithm libraries make it easy to enable interactive queries and deliver fast responses.
- Develop a wide range of algorithms. Spark lets you develop and deploy all workloads—including machine learning, iterative and batch—faster.
- Accelerate analytics results. Spark uses an in-memory approach for processing data to deliver results quickly.
Since it was first created in 2009, Spark has become one of the most active open source projects, with more than 400 contributors. Given the advantages of Spark, it should be no surprise that it’s quickly gaining popularity among data scientists, developers and line-of business executives.
How is Spark driving the insight economy forward? Organizations in numerous industries are using Spark to analyze large volumes of data and deliver real-time insights while simplifying software development. Optimizing marketing promotions Spark can help retailers transform huge collections of customer data into insights that inform marketing campaigns and targeted promotions. They can also use Spark to create algorithms for fine-tuning marketing efforts in progress.
Using Spark’s machine-learning capabilities, data scientists can produce algorithms that automatically adjust the amount of offered discounts or the timing of communications based on success rates so far. Improving telecom performance Telecom and content providers can use Spark to deliver the highest-quality service to customers.
Data scientists can create algorithms that analyze real-time performance data and make network adjustments to help ensure customers have consistent, high-quality voice or video quality. Forging new frontiers in science from mapping genomes to modeling air turbulence, scientific experiments today generate massive data volumes. Building algorithms with Spark helps research groups produce results fast so they can fine-tune experiments and explore more permutations without extending the duration of projects. By simplifying programming, Spark helps researchers stay focused on science and avoid complex programming.
IBM analytics solutions are ready-made for incorporating new technologies into existing architectures and delivering rapid business benefits. These flexible solutions can take advantage of whatever Spark-based innovations lie ahead as more and more data scientists around the globe create solutions using Spark. Ready to see how Apache Spark can help you profit in the insight economy? Visit IBM/Spark