Systems | Development | Analytics | API | Testing

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How Traveloka built a Data Provisioning API on a BigQuery-based microservice architecture

To build and develop an advanced data ecosystem is the dream of any data team, yet that often means understanding how the business will need to store and process that data. As Traveloka’s data engineers, one of our most important obligations is to custom-tailor our data delivery tools for each individual team in our company, so that the business can benefit from the data it generates.

Apigee API Monitoring: Find & Fix Issues Fast

Almost every app and digital interaction today depends on APIs, so it’s important to be able to find and fix issues fast. Apigee’s API monitoring can alert you to live issues, give you in-depth details for every problem, and recommend a course of action. Take a look at this API monitoring demo from the Apigee team to keep your APIs running smoothly!

Cloudera 2.0: Cloudera and Hortonworks Merge to form a Big Data Super Power

We’ve all dreamed of going to bed one day and waking up the next with superpowers – stronger, faster and even perhaps with the ability to fly. Yesterday that is exactly what happened to Tom Reilly and the people at Cloudera and Hortonworks. On October 2nd they went to bed as two rivals vying for leadership in the big data space. In the morning they woke up as Cloudera 2.0, a $700M firm, with a clear leadership position. “From the edge to AI”…to infinity and beyond!

What I've learnt from 15 years of mentoring

As an entrepreneur and a CEO of a startup, I think it’s vital to have a mentor. You often come to critical junctures and that’s when it can be invaluable to speak to someone. You can’t talk to your colleagues though because you need to motivate them and keep the organization growing. That’s why having an external mentor is very helpful.

How to transfer BigQuery tables between locations with Cloud Composer

BigQuery is a fast, highly scalable, cost-effective, and fully-managed enterprise data warehouse for analytics at any scale. As BigQuery has grown in popularity, one question that often arises is how to copy tables across locations in an efficient and scalable manner. BigQuery has some limitations for data management, one being that the destination dataset must reside in the same location as the source dataset that contains the table being copied.