As we covered in our recent NLP blog, there are a lot of cool use cases for text / sentiment analysis. One recent instance we found really interesting came out of our May presentation at SeaTUG (Seattle Tableau User Group.)
We’ve recently experienced two fairly large system problems that have affected approximately 35% of our clients. The first issue took 50 minutes to resolve and the other approximately 10 hours. The root cause in both cases was the way we handled the provisioning of adhoc sandboxes on top of our SnowflakeDB (a few words about “how we started w/ them”).
How do you protect the stability of the work you’ve already done, while evangelizing experimentation, exploration and progress within your organization? The rapid evolution in business intelligence and analytics capabilities is both exhilarating and overwhelming.
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.
APIs are a key way businesses operate at the speed and scale necessary for today’s economy. We designed Apigee, Google Cloud’s full lifecycle API Management platform, to give businesses control over, and visibility into, the APIs that connect applications and data across the enterprise and across clouds. Apigee was recently recognized by Gartner as a leader in the 2018 Magic Quadrant for Full Life Cycle API Management.
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.
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!
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.
