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Couchbase Analytics: Customers’ Moments of Truth Revealed!

Introduction

I recently joined Couchbase’s product team to lead Couchbase Analytics. This Analytics Service enables our customers to measure near-real time business operations, derive insights from data, and drive agile decisions to expand business growth.

Within my first few weeks at Couchbase, I had the opportunity to meet with a number of customers to learn about the “why” and the “how” behind their usage of Couchbase Analytics. This exercise helped my team think backwards from the customer point of view and assess which features customers care about most. More importantly, we learned how we could further take action on their feedback. In this blog, I’ll discuss:

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Couchbase N1QL: To Query or To Analyze? Part 2

When you need to query documents using SQL, there are two options available in Couchbase. The Query service and the Analytics service. Our blog, N1QL: To Query or To Analyze? provides a detailed overview of both services. I highly recommend reading it before this one. This article aims to expand on the previous blog by adding some concrete, hands-on examples. For each example we’ll cover how to write the query in both services and we’ll look at the performance differences. The goal is that readers will walk away with more knowledge to help quickly identify patterns and use cases that best fit each service.

Summary

Before jumping into examples. Let’s refresh ourselves on the high-level key characteristics of the two services.

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Deep Dive: Window Functions in Couchbase Analytics

Window Functions

Couchbase Server 6.5 Beta brings a host of new features to the leading NoSQL database. One of the key additions to the N1QL query language is support for window functions. These functions were originally introduced in the SQL:2003 standard and provide a performant way of answering many complex business queries. Window functions were previously discussed in this series of posts [2], [3], [4]. In this installment, we’ll dive deep into their implementations in Couchbase Analytics.

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The Couchbase Analytics service [5] is designed to handle complex ad-hoc queries in the Couchbase data platform. Its key component is the MPP query engine that runs on a separate set of nodes in the cluster to guarantee workload isolation for the operational data nodes. Data is ingested into Analytics using the DCP change protocol [6] and is hash-partitioned among all available Analytics nodes. The MPP query processor divides a single query into subtasks and schedules those to run in parallel on all nodes, repartitioning data if necessary.

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Build a Real-Time Data Visualization Dashboard With Couchbase Analytics and Tableau

Introduction

Couchbase Server is a hybrid NoSQL database that supports operational and analytical workloads. Couchbase Analytics in Couchbase Server 6.0 brings "NoETL for NoSQL," enabling users to run ad-hoc analytical queries on JSON data in their natural form — without the need for transformation or schema design — by leveraging a massively parallel processing (MPP) query engine.

Every enterprise has already invested in a visualization tool and therefore has a critical need to leverage existing investments. This includes not only tooling but also skillsets and training of business reporting and dash-boarding teams.