Hiring additional data engineers is a problem, not a solution

Adding additional engineers to a messy data problem is usually counterproductive.

The Biotech data dilemma and the Mammoth solution

The Biotechnology, Bioinformatics and Computational Biology fields are growing on multiple fronts, including data volume and complexity. The amount of data generated is far larger than the ability to consume or make sense of it. While data volume may sound like a problem, there is another more significant and pressing issue, specifically around existing processes and general accessibility to data.

JSON extraction made simple

Problem — JSON handling can get complex for developers. It’s inaccessible for non-developers. Solution — Auto key-value detection, quick transformation into rows and columns, powerful automation, zero coding

Turn multiple incompatible files into a clean master dataset

If you struggle with data cleansing, normalization, standardization or consolidation, this article is for you. Mammoth’s powerful new feature will save you time, money and all the headaches.

Combine two tables — by time series — without coding

Here’s a scenario (that happens to be typical in the manufacturing world) — Dataset A (ERP data) contains a date range, and Dataset B (Sensor Data) contains rows that fall within the date range of dataset A.

Announcing our partnership with Segment

Announcing our partnership with Segment. We are excited to announce that Mammoth is now a destination in Segment’s rich catalog. Learn more.