The practice of data management is one that requires technical acumen, but there are also many policy and regulatory issues that inform and influence the design of our systems. With the introduction of legal frameworks such as the EU GDPR and California’s CCPA it is necessary to consider how to implement data protectino and data privacy principles in the technical and policy controls that govern our data platforms. In this episode Karen Heaton and Mark Sherwood-Edwards share their experience and expertise in helping organizations achieve compliance. Even if you aren’t subject to specific rules regarding data protection it is definitely worth listening to get an overview of what you should be thinking about while building and running data pipelines.
Do you want to try out some of the tools and applications that you heard about on the Data Engineering Podcast? Do you have some ETL jobs that need somewhere to run? Check out Linode at dataengineeringpodcast.com/linode or use the code dataengineering2019 and get a $20 credit (that’s 4 months free!) to try out their fast and reliable Linux virtual servers. They’ve got lightning fast networking and SSD servers with plenty of power and storage to run whatever you want to experiment on.
Datacoral is this week’s Data Engineering Podcast sponsor. Datacoral provides an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to construct its infrastructure. Datacoral’s customers report that their data engineers are able to spend 80% of their work time invested in data transformations, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from mere terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral for more information.
What happens when your expanding log & event data threatens to topple your Elasticsearch strategy? Whether you’re running your own ELK Stack or leveraging an Elasticsearch-based service, unexpected costs and data retention limits quickly mount. Now try CHAOSSEARCH. Run your entire logging infrastructure on your AWS S3. Never move your data. Fully managed service. Half the cost of Elasticsearch. Check out this short video overview of CHAOSSEARCH today! Forget Elasticsearch! Try – search analytics on your AWS S3.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
- This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more.
- Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at dataengineeringpodcast.com/chaossearch and don’t forget to thank them for supporting the show!
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
- Your host is Tobias Macey and today I’m interviewing Karen Heaton and Mark Sherwood-Edwards about the idea of data protection, why you might need it, and how to include the principles in your data pipelines.
- How did you get involved in the area of data management?
- Can you start by explaining what is encompassed by the idea of data protection?
- What regulations control the enforcement of data protection requirements, and how can we determine whether we are subject to their rules?
- What are some of the conflicts and constraints that act against our efforts to implement data protection?
- How much of data protection is handled through technical implementation as compared to organizational policies and reporting requirements?
- Can you give some examples of the types of information that are subject to data protection?
- One of the challenges in data management generally is tracking the presence and usage of any given information. What are some strategies that you have found effective for auditing the usage of protected information?
- A corollary to tracking and auditing of protected data in the GDPR is the need to allow for deletion of an individual’s information. How can we ensure effective deletion of these records when dealing with multiple storage systems?
- What are some of the system components that are most helpful in implementing and maintaining technical and policy controls for data protection?
- How do data protection regulations impact or restrict the technology choices that are viable for the data preparation layer?
- Who in the organization is responsible for the proper compliance to GDPR and other data protection regimes?
- Downstream from the storage and management platforms that we build as data engineers are data scientists and analysts who might request access to protected information. How do the regulations impact the types of analytics that they can use?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
- Data Protection
- This Is DPO
- Intellectual Property
- European Convention Of Human Rights
- CCPA == California Consumer Privacy Act
- PII == Personally Identifiable Information
- Privacy By Design
- US Privacy Shield
- Principle of Least Privilege
- International Association of Privacy Professionals
- Data Provenance
- Chief Data Officer
- UK ICO (Information Commissioner’s Office)
- Data Council