Executive Summary for the Report

Business intelligence technologies are well established in many organizations - half of the organizations that Aberdeen surveys have been using BI for 5 years or more. But, despite this wealth of experience, Aberdeen has found that only 43% of business intelligence projects are delivered on-time or early.

Agile BI is business intelligence that can rapidly adapt to meet changing business needs. As emerging business events require managers to have access to new or different information, an agile BI implementation can quickly deliver that information - through manipulation by the business users themselves or by IT professionals.

Fast Facts Extracted from the Agile BI Report

Best-in-Class companies are more than twice as likely as all others to provide BI to their end-users that is fully interactive. That is, all parts of the BI presentation can be used to manipulate the underlying information in some way. With a highly responsive, fully interactive tool at their disposal business managers can effectively explore data as fast as their creativity and imagination will let them.

Forty-three percent (43%) of enterprises report that making timely decisions is becoming more difficult. Managers increasingly find they have less time to make decisions after business events occur. Alternatively, managers are likely to need more - or different - information in order to support their decisions effectively.

Jammed between a rock and a hard place, organizations of all types are fairly united in the approach they plan to take to manage the flood of data on one hand and the expectations of business managers on the other. Enterprises are working to streamline their IT organization and simultaneously make executives and managers more self-sufficient in their use of BI.

Analyst Recommendation for Implementing Agile BI

Success requires close collaboration between IT and BI professionals and the business users concerned. A BI center of excellence can provide a solid foundation for BI projects by providing appropriate data, software tools and training.

For their part, business managers need to be willing to undergo a change in culture and working practices. They need to get "hands-on" to interact with and manipulate data if they are to meet the shrinking timeframe for business decisions that they face.

What Are Some Recommendations for Implementing Spark for a BI Platform?

Implementing Apache Spark for a Business Intelligence (BI) platform can unlock significant potential for processing large volumes of data, performing complex analytics, and generating actionable insights in real-time. Here are some recommendations for effectively implementing Spark within a BI platform:

  1. Understand Data Sources and Requirements: Before implementing Spark, thoroughly understand the data sources, volume, velocity, and variety of data that will be processed. Identify the specific BI use cases and requirements, such as real-time analytics, ad-hoc querying, or predictive analytics, to tailor the Spark implementation accordingly.

  2. Data Preparation and ETL: Leverage Spark's powerful capabilities for data preparation and Extract, Transform, Load (ETL) processes. Use Spark's APIs and libraries such as Spark SQL, DataFrame API, and Spark MLlib to clean, transform, and preprocess raw data from various sources before loading it into the BI platform for analysis.

  3. Optimize Data Processing Performance: Optimize Spark job performance by tuning configuration parameters, optimizing resource allocation, and leveraging techniques such as partitioning, caching, and data locality. Utilize Spark's advanced features like RDD persistence, data shuffling optimization, and parallel processing to improve data processing efficiency and reduce job execution times.

  4. Scale Out for Big Data: Spark excels at processing large-scale datasets distributed across clusters of nodes. Implement a scalable architecture for the BI platform using Spark clusters to handle big data workloads effectively. Consider factors such as cluster sizing, resource management, fault tolerance, and high availability to ensure seamless scalability and reliability.

  5. Choose Appropriate Storage Options: Select appropriate storage options for storing intermediate data and persisted results within the Spark ecosystem. Utilize distributed storage solutions such as Hadoop Distributed File System (HDFS), Apache Parquet, or Apache Avro for efficient data storage and retrieval. Leverage data partitioning and columnar storage formats to optimize query performance and minimize data processing overhead.

  6. Integrate with BI Tools and Visualization Platforms: Integrate Spark seamlessly with BI tools and visualization platforms to enable interactive querying, data exploration, and visualization of insights. Leverage connectors, APIs, or libraries provided by BI vendors to integrate Spark with popular BI tools such as Tableau, Power BI, or QlikView. Ensure compatibility and interoperability between Spark and BI tools to facilitate seamless data analysis and reporting workflows.

  7. Implement Real-Time Analytics: Leverage Spark Streaming or Structured Streaming to implement real-time analytics capabilities within the BI platform. Process and analyze streaming data in near real-time to derive actionable insights, monitor key metrics, and detect anomalies or trends as they occur. Implement streaming ETL pipelines to ingest, process, and analyze streaming data streams from various sources in real-time.

  8. Ensure Data Security and Compliance: Implement robust data security and compliance measures to protect sensitive data and ensure regulatory compliance within the BI platform. Utilize authentication, authorization, encryption, and auditing features provided by Spark and underlying data storage systems to secure data at rest and in transit. Implement role-based access control (RBAC) and data masking techniques to restrict access to sensitive information and enforce data privacy regulations.

  9. Monitor and Manage Performance: Implement comprehensive monitoring and performance management tools to monitor Spark job execution, resource utilization, and cluster health in real-time. Use monitoring dashboards, logs, and alerts to identify performance bottlenecks, optimize resource allocation, and troubleshoot issues proactively. Implement workload management policies and job scheduling mechanisms