Discover And De-Clutter Your Unstructured Data With Aparavi
Data Engineering Podcast
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Summary
Unstructured data takes many forms in an organization. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc. Another category of unstructured data that every business deals with is PDFs, Word documents, workstation backups, and countless other types of information. Aparavi was created to tame the sprawl of information across machines, datacenters, and clouds so that you can reduce the amount of duplicate data and save time and money on managing your data assets. In this episode Rod Christensen shares the story behind Aparavi and how you can use it to cut costs and gain value for the long tail of your unstructured data.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
- Your host is Tobias Macey and today I’m interviewing Rod Christensen about Aparavi, a platform designed to find and unlock the value of data, no matter where it lives
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Aparavi is and the story behind it?
- Who are the target customers for Aparavi and how does that inform your product roadmap and messaging?
- What are some of the insights that you are able to provide about an organization’s data?
- Once you have generated those insights, what are some of the actions that they typically catalyze?
- What are the types of storage and data systems that you integrate with?
- Can you describe how the Aparavi platform is implemented?
- How do the trends in cloud storage and data systems influence the ways that you evolve the system?
- Can you describe a typical workflow for an organization using Aparavi?
- What are the mechanisms that you use for categorizing data assets?
- What are the interfaces that you provide for data owners and operators to provide heuristics to customize classification/cataloging of data?
- How can teams integrate with Aparavi to expose its insights to other tools for uses such as automation or data catalogs?
- What are the most interesting, innovative, or unexpected ways that you have seen Aparavi used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Aparavi?
- When is Aparavi the wrong choice?
- What do you have planned for the future of Aparavi?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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