Today’s organizations generate data at massive scales and volumes.
Applications running on servers in the cloud, data centers and edge devices all produce more data, from more data sources, than ever before. With this growth, enterprises constantly grapple with efficiently using their operational data to gather business insights using a variety of data analytics.
Unfortunately, traditional approaches to analytics spread operational data across multiple disparate systems. These systems have varied data structures and exhibit different transactional properties making it difficult to analyze by end users. Developers often address this challenge by creating non-standard data analytic pipelines and architectures that invariably impact operational (transactional) applications.
Organizations find themselves having to deal with non-shared processes and systems across teams. This exacerbates the problem and ultimately leads to increased administration costs, data leakage and governance obstacles.
As data and transactions emerge from modern applications, there is a greater need to enrich the user experience. For example, it’s now possible to weave together operational and analytical workloads while ingesting and processing real-time data. Businesses want to offer robust data products that help them understand user behavior better. Likewise, developers want to analyze data with ease and without incurring tremendous infrastructure costs.
JSON Data Analytics for the Win…But Wait!
In modern applications, JSON has become a de facto standard for storing data that requires schema flexibility.
Extending data stored in the JSON format is relatively simple, allowing applications to evolve quickly and satisfy business needs. But traditional data architectures and tools in the data analysis landscape impose another restriction: They separate Transactional (OLTP) and Analytical (OLAP) workloads. Data analysis tools then need to wait for the data pipelines to be modified when underlying schemas change.
Extract-Transform-Load (ETL) processes incur high costs just so that the data is available for further analytical processing when needed. This multi-step approach makes data management challenging due to the complexity of using separate systems. As a result, the inherent delays between OLTP and OLAP systems slow decision-making and cripple an otherwise agile business.
Naturally, real-time data is essential for improving the output of advanced data analytics and business intelligence applications. This impacts the ability to identify trends, to apply machine learning, or to harness any other kind of prescriptive analytics in a timely manner.
Couchbase Analytics: Simplified Data Analytics, Processing & Management
Enter the era of hybrid analytics with Couchbase Analytics.
We tried to reimagine a world where our customers win and analytics are available at the speed of transactions. This is a world where ETL processes are non-existent and a single hybrid system breaks down the wall between transactional and analytical workloads.
At its very core, Couchbase Analytics avoids moving data from databases to data warehouses and provides access to real-time data processing with ease.
Couchbase Analytics also processes information from other systems in addition to those present in a Couchbase cluster. This innovation enables enhanced customer experiences and a better understanding of business performance leading to more data-driven decisions.
Developers can now perform complex ad-hoc analytical requests – large JOINs, aggregations, grouping – using parallel query-processing engines with bulk data handling.
Let Data Drive Your Business Decisions for Competitive Advantage
With real-time data analytics, you now experience faster time to insight.
You can run more experiments to better understand your customers as there is no laborious ETL involved. The feedback loop is also shorter between the transaction and analytical systems without an ETL layer.
Drill-down from analytic aggregates always leads to fresh application data. As a result, developers and database administrators avoid database sprawl and no longer manage separate analytical systems.
Couchbase Analytics also offers a rich SQL-like database query language that makes exploring and analyzing JSON data a breeze. (Learn more about N1QL here.)
Finally, it’s a cheaper approach: a single hybrid system consumes less infrastructure and needs fewer copies of data, resulting in a lower total cost of ownership. By separating the workload betweens operational queries and analytical workloads, resource utilization is more effective without the risk of impacting the transactional system. (I’ll explore more of the benefits of workload isolation in our next post of this series.)
Insight-Driven Applications for the Modern Enterprise
Enterprises use Couchbase Analytics across a number of industries, including:
- Fintech and insurance companies detect fraud and score risks in real-time across transactions, policies and claims.
- Ecommerce and B2C applications generate personalized recommendations based on session activity.
- Companies in every sector optimize marketing and ad campaigns to avoid the delays common to current batch systems.
- IoT platforms detect device issues and optimize processes in real time and not wait for periodic maintenance activities.
In the next blog post of this series, we will explore how Couchbase Analytics improves efficiency and lowers risk by isolating the analytic and operational workloads.