SQL Server Analysis Services (SSAS) is a powerful tool that allows users to analyze and process large amounts of data. In recent years, there has been a growing interest in Big Data and its potential applications. This article will discuss the integration of SSAS with Big Data technologies, such as Hadoop and Hive, and explore the benefits and limitations of this approach.
The Rise of Big Data
Big Data has become a buzzword in the tech industry, and for good reason. The amount of data being generated and collected is growing exponentially, and organizations are looking for ways to extract valuable insights from this data. According to predictions, the growth of Big Data is expected to be 60% per year until 2016.
Challenges in Big Data Analytics
While the need for Big Data solutions is increasing, there is currently no single solution available in the market that can address all the challenges. Similarly, in the business intelligence ecosystem, there is no one-size-fits-all solution. However, organizations are actively exploring different approaches to tackle these challenges.
Introducing Klout and its Big Data Solution
Klout is a popular social media platform that aims to measure the influence of individuals. To overcome the challenge of low user engagement, Klout has implemented a system where visitors can give “K+” to other users in a particular area. This has helped Klout in retaining and engaging its users.
As a result of its activities, Klout has become a big consumer of Big Data and an early adopter of Hadoop-based systems. With approximately 1 trillion rows of data and a thousand terabyte warehouse, Klout needed an efficient way to analyze and process this data.
Integrating SQL Server Analysis Services with HiveQL
While HiveQL, the language used for Big Data analytics, supports ad-hoc queries, there are always better solutions available. One such solution is the integration of SQL Server Analysis Services (SSAS) with HiveQL. Although there is no direct method to achieve this integration, there are workarounds that have been implemented.
A new ODBC driver from Klout has broken through the limitation, allowing the SQL Server Relation Engine to be used as an intermediate stage before SSAS. This approach enables Klout to leverage the power of SSAS for Big Data analytics.
Benefits and Limitations
The integration of SSAS with HiveQL offers several benefits. It allows organizations to leverage their existing SSAS infrastructure and take advantage of its powerful analytical capabilities. Additionally, SSAS provides a familiar and user-friendly interface for data analysis.
However, there are limitations to this approach. Direct connectivity between SSAS and HiveQL is not possible, and pass-through queries to linked servers are required. This adds complexity to the setup and may impact performance.
Best Practices and Lessons Learned
A white paper has been published that delves into the concepts discussed in this article in greater depth. It covers topics such as the Klout Big Data solution, Big Data analytics based on Analysis Services, Hadoop/Hive and Analysis Services integration, limitations of direct connectivity, and best practices and lessons learned.
Reading this white paper can provide valuable insights into how Klout has successfully utilized SQL Server Analysis Services and Big Data technologies to enhance its offerings and improve efficiency.
Conclusion
SQL Server Analysis Services offers a powerful solution for analyzing and processing large amounts of data. By integrating SSAS with Big Data technologies like Hadoop and Hive, organizations can leverage the benefits of both worlds. While there are limitations to this approach, it provides a viable solution for organizations looking to harness the power of Big Data analytics.
So, if you’re interested in exploring the possibilities of SQL Server Analysis Services and Big Data, I encourage you to read the white paper mentioned in this article. It’s a great resource for understanding the concepts and best practices in this field.