Submissions for Data Stores track
Guide on how to select datastores to solve different problems
This is a call for Submissions for the Data Stores conferences that will be held between September 2021 and August 2022 for the Data Stores track under Rootconf. Choosing Datastores conferences aim to help technology practitioners learn about how to select a datastore and be aware of their limitations when applied to the problem at hand.
We are accepting experiential talks on:
We invite engineers who work closely with datastores to speak about their experience in selecting, using and scaling these technologies.
Contact information: Join the Rootconf Telegram group at https://t.me/rootconf or follow @rootconf on Twitter.
For inquiries, contact Rootconf at email@example.com or call 7676332020.
Not accepting submissions
We are accepting experiential talks and written content on:
Content can be submitted in the form of:
Submissions will be peer-reviewed by the editor Yagnik Khanna.
Optimising configurations @ Uber with dual store strategy
Most of the software services are driven by internal configurations. There is no one-size-fits-all solution for these configurations as they could be as simple as some basic flags to full-blown multi-page JSONs. One common pitfall across these services is that the configurations are hardcoded in the codebase as JSON, YAML files. Customizations are hard to maintain, difficult to change, and prohib… more
30 min talk
Migrating Online Data
Relational Databases as well as non relational data stores support a number of high performing, high volume and highly available applications on the Internet. At Linkedin, many important functionalities are powered by an RDBMS (MySQL and Oracle) or a NoSQL Data Store (Espresso). While we’ve developed a reliable process for schema evolution, we have also run into major changes in the fundamental s… more
When, why and what database to choose for time-series data analytics?
Introduction Time series database (TSDB) is optimized for storing and serving data through associated pairs of time and value. They are different from other datastores that track changes to the overall system as INSERTs not UPDATEs. TSDB largely help in forecasting and anomaly detection with seamless application of moving average, exponential smoothing, stationarity, autocorrelation, SARIMA, and … more
15 min talk