One of the most challenging aspects of building a data platform has nothing to do with pipelines and transformations. If you are putting your workflows into production, then you need to consider how you are going to implement data security, including access controls and auditing. Different databases and storage systems all have their own method of restricting access, and they are not all compatible with each other. In order to simplify the process of securing your data in the Cloud Manav Mital created Cyral to provide a way of enforcing security as code. In this episode he explains how the system is architected, how it can help you enforce compliance, and what is involved in getting it integrated with your existing systems. This was a good conversation about an aspect of data management that is too often left as an afterthought.
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Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
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- Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt.
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- Your host is Tobias Macey and today I’m interviewing Manav Mital about the challenges involved in securing your data and the work that he is doing at Cyral to help address those problems.
- How did you get involved in the area of data management?
- What is Cyral and what motivated you to build a business focused on addressing data security in the cloud?
- Can you start by giving an overview of some of the common security issues that occur when working with data?
- What new security challenges are introduced by building data platforms in public cloud environments?
- What are the organizational roles that are typically responsible for managing security and access control to data sources and repositories?
- What are the tensions, technical or organizational, that lead to a problematic or incomplete security posture?
- What are the differences in security requirements and implementation complexity between software applications and data systems?
- What are the data systems that Cyral integrates with?
- How did you determine what platforms to prioritize?
- How does Cyral integrate into the toolchains used to deploy, maintain, and upgrade an organization’s data infrastructure?
- How does the Cyral platform address security and access control of data across an organization’s infrastructure?
- How are schema changes handled when using Cyral to enforce access control to PII or other attributes?
- How does Cyral help with reducing sprawl of data across unmonitored systems?
- What are some of the most interesting, unexpected, or challenging lessons that you learned while building Cyral?
- When is Cyral the wrong choice?
- What do you have planned for the future of the Cyral platform?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?