Systems | Development | Analytics | API | Testing

Astera

Transcript Processing with AI-Powered Extraction Tools: A Guide

The class of 2027 saw a massive influx of applications at top universities across the United States. Harvard received close to 57,000 applications for the class of 2027, while MIT received almost 27,000. UC Berkeley and UCLA, meanwhile, received 125,874 and 145,882 respectively. Manual transcript processing is an uphill battle for educational institutions at every level.

Harnessing Data Extraction in Education for Insightful Solutions

The education sector has always worked with data to guide various processes, most notably student progress. But with powerful, AI-driven data extraction tools impacting other industries, it's time for educators to leverage these tools, accelerate data extraction, and turn data into actionable insights much faster.

XML JSON File Source in Astera Data Stack

In this video, we will be discussing how you can extract data from an XML JSON File Source using the XML JSON File Source object in Astera. XML JSON file source in Astera provides a high-speed reader for XML or JSON files in which the data is stored in a hierarchical format within several nodes and sub nodes. Contents of the video: Configure the object properties, including File Path, Schema Location, and other options. Generate XML Schema. Customizing XML Layout and Config Parameters. XML data preview.

Delimited File Source in Astera Data Stack

In this video, we will be discussing how you can extract data from a delimited file using the Delimited File Source object in Astera. Delimited files are one of the most commonly used data formats and use specific characters or delimiters to separate records and indicate the structure of the table in which the data resides. Contents of the video.

Database Table Source in Astera Data Stack

In this video, we will see how a Database Table Source object works in Astera. The Database Table Source object is used to retrieve data from a database table. It offers incremental reads via change data capture, supports multi-way partitioning for enhanced performance, and allows customization with WHERE clauses and sorting options.