Efficient Enterprise Data Integration Strategy with Connectors – Google BigQuery and MuleSoft Anypoint Connector

One of the main challenges faced by today’s enterprise architects or IT heads is finding a way to quickly plug in data-driven intelligence into their enterprise applications. The most common way to address this need is to implement integration platforms like AnyPoint in your enterprise data architecture. While many standard integrations such as CRMs and ERPs are readily available, integrations with sophisticated analytics tools is still a challenge. Organizations have to invest in building these integrations, which are time-consuming, and costly. This dictates the need to approach enterprise data integration in new ways that are quick to develop and deploy. Custom connectors can help tackle these challenges efficiently.

Let’s take the case of Google BigQuery for example. We all know how MuleSoft’s Anypoint platform is one of the leading enterprise application integration platforms within the industry. It has powerful features and can easily fit within your enterprise ecosystem. However, if your organization is using BigQuery as your cloud data warehouse, integrating it within your application ecosystem even through Anypoint is going to be a time-consuming process.

Why Google BigQuery?

BigQuery is a serverless and highly scalable data store from Google Cloud. Industry-grade encryption for your data – both at rest and in transit – and highly granular access control and governance features built into the offering ensure that your data is at least as secure as it would be in your own data centers. What’s more, it comes with its machine learning engine built-in and lets you leverage its ML chops using SQL tools! That is why true global leaders such as HSBC, UPS, and Dow Jones leverage BigQuery’s tremendous scale and speed for their needs for data-driven intelligence.

You will need to tackle several issues when it comes to building actual data integrations between your systems and BigQuery:

  • Silos of data owned by a variety of Enterprise systems – ERP, CRM, HR systems and purpose-built customer-facing systems.
  • The need to develop and maintain data/ETL pipelines between your systems and BigQuery.
  • Security and operation of all the infrastructure involved in this setup
  • Applying results from the analytics back into the relevant systems.

If these are left unresolved, adding BigQuery to the mix results in creating one more silo to deal with.

MuleSoft BigQuery Connector

GS Lab’s MuleSoft Certified BigQuery Connector for the Anypoint Platform has the potential to solve these problems for you while cutting down your development effort drastically. The connector interfaces with the Google BigQuery API to leverage analysis of massive datasets working in conjunction with Google storage if needed. It allows for the management, ingestion, and querying of data in BigQuery.

Since the BigQuery connectivity is made available directly in MuleSoft flows, you can easily send data from all your applications connected to MuleSoft to BigQuery. Plus, you can leverage BigQuery’s ability to combine data from various systems to derive intelligence, helping break down the silos. With MuleSoft providing the connectivity between the two endpoints, the need for custom pipelines goes away. Streaming in MuleSoft ensures that you can also incorporate live data streams if needed. And, all of this can be done using the same system that has already gone through approvals and deployment cycles, no custom setups needed.

With this robust connectivity, you can tackle novel problems like:

  • Personalize customer experiences by collating customer information from disparate systems, profiling them in BigQuery, and then transferring back data from the profile insights.
  • Improve processes by feeding workflow data from your applications into BigQuery, making predictions on various process parameters, and then feeding the results back into the workflows for faster and more efficient processes.
  • Detect anomalies by running transaction and logistical data into BigQuery, isolating unusual patterns using machine learning and pulling back their details for reporting and preventative action.

With the BigQuery Connector now providing the crucial link between the two power systems, you can accelerate your analytics projects from a matter of weeks to mere hours of development time. That, too, without having to look for new skills or adding new complexity to your IT setups.

Author
Viraj Paripatyadar | Senior Manager – Business Development

Viraj has been solving varied customer problems for years. In his 15 years at GS Lab, Viraj has contributed to, developed, or designed a range of products for various customers of GS Lab across the globe. Starting originally from a strong background in Web application security, he has expanded his area of work to include everything related to the Web, Web-based ecosystems and their applications in enterprise use cases. His main current interest is finding intersections of AI/ML technology and the world of FinTech. Currently, Viraj works as a Senior Manager of Business Development at GS Lab and is responsible for Enterprise Sales.