Analytics

BigQuery, Google Cloud’s serverless, highly scalable, low-cost, enterprise data warehouse, was designed to make data analysts productive. With no infrastructure to manage, customers can focus on analyzing data using familiar Standard SQL, while simplifying database administration and data operations. Large enterprises, mid-market growing organizations, and cloud native startups across the globe can use BigQuery to perform analytics at scale with equal ease.

Automating the invoicing process to cut down up to 2-3 mandays per month. Our goal was clear. But how did we do it? Assisting clients with data and automation is our specialty here at Keboola, with the mission of helping businesses become “data-driven.” Several months ago it was our turn to put in moto our own engines and build an automated workflow for our invoicing process.

For part two of this blog I’m seated in a café in Sydney Harbour in the shadow of the Opera House and in full view of Sydney Harbour Bridge. Why’s that significant? Well it’s not really, other than this is the 2nd stop on our Data Revolution Tour where the keynote I delivered was essentially on this very topic. Let me explain.

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.