The Data Warrior

Changing the world, one data model at a time. How can I help you?

How to manage GDPR compliance with Snowflake’s Time Travel and Disaster Recovery 

One year after implementation, the European Union’s General Data Protection Regulation (GDPR) continues to be a hot regulatory topic. As organizations work to bring their data practices into compliance with the new law, one question comes up repeatedly: How does Snowflake, the data warehouse built for the cloud, enable my organization to be GDPR compliant?

Check out my latest post to see my answer:

How to manage GDPR compliance with Snowflake’s Time Travel and Disaster Recovery | Snowflake Blog

Cheers!

Kent

The Data Warrior

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The Elephant in the Data Lake and Snowflake

So is Hadoop finally dead? For many use cases, I think it really is. The cloud and the continued evolution of technology has created newer, better ways of working with data at scale. Check out what Jeff has to say about it!

Jeffrey Jacobs, Consulting Data Architect, Snowflake Certified Data Practitioner.

Let’s talk about the elephant in the data lake, Hadoop, and the constant evolution of technology.

Hadoop, (symbolized by an elephant), was created to handle massive amounts of raw data that were beyond the capabilities of existing database technologies. At its core, Hadoop is simply a distributed file system. There are no restrictions on the types of data files that can be stored, but the primary file contents are structured and semi-structured text. “Data lake” and Hadoop have been largely synonymous, but, as we’ll discuss, it’s time to break that connection with Snowflake’s cloud data warehouse technology.

Hadoop’s infrastructure requires a great deal of system administration, even in cloud managed systems.   Administration tasks include: replication, adding nodes, creating directories and partitions, performance, workload management, data (re-)distribution, etc.  Core security tools are minimal, often requiring add-ons. Disaster recovery is another major headache.  Although Hadoop is considered a “shared nothing” architecture, all…

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Schema-on-what? How to model JSON

It seems hard to believe, but all year, around the world, I continue to have this conversation on whether or not we still need data modeling.

I know! Crazy!

Thought we were past that…

As I have said before,

Schema-on-read has the word SCHEMA in it!

So instead of continuing to rant about it, I decided to put together a talk to show people, graphically, what I meant by decomposing, step by step, a few JSON documents into real data models. For the sake of the talk I decided to go with 3NF and Data Vault styles to make my point.

This talk has been very well received so I decided I would share it a bit more publicly by posting it here on my blog.

 

Now that you can see how to model JSON, check out my Snowflake ebook on how to easily analyze JSON using SQL.

If you know any meet-ups or conferences that I should be giving this talk at, please let me know. Or check out my speaking schedule for 2019 and join me at one of the events already on my calendar. (1st up is ITOUG in Milano!)

Ciao!

Kent

The Data Warrior & Chief Evangelist at Snowflake

P.S. There was no magic, or built-in wizard, to creating the models. I did it all by hand using Oracle Sql Developer Data Modeler.

 

Automatic Clustering, Materialized Views and Automatic Maintenance in Snowflake 

Boy are things going bananas at Snowflake these days. The really big news a few weeks back was another round of funding! This time we got another $450 million.

A-MAZ-ING!

We are gaining marketshare everyday with name brands signing up in droves. I am constantly taken aback by the names I see on our win-wire. In my role as evangelist, I am getting to speak with folks at places like Sony, NBC Universal, and CapitalOne to name a few. Not only here in the US but over in EMEA as well (as my frequent flyer status will attest). And early next year I will be heading out to Australia and New Zealand too (stay tuned for details).

As exciting as all that is (#ILoveMyJob), even more exciting is how our engineering team is putting all that funding $$ to use to continue to improve what is already the leading cloud-built data warehouse. This week we announced two new major features.

From today, two new automation and performance capabilities are available in Snowflake. They are the latest advancements to Snowflake’s ability to deliver the best performance and scalability without requiring administration or maintenance from our customers. They also lay the foundation for a series of new features to come in the months ahead.

Read all the details here: Automatic Clustering, Materialized Views & Automatic Maintenance in Snowflake

After reading up on these features, head on over to our site and sign up fro your FREE trial account to see for yourself how awesome, and easy, moving to the cloud can be.

Make it snow!

Kent

Chief Technical Evangelist, Snowflake

The Data Warrior

Get Started Faster with Snowflake Partner Connect

Got data you want to load and analyze in the cloud? Would you like to do it today? The check out this announcement about getting up and running with your data in Snowflake faster. #YourDataNoLimits

Ending the struggle for data starts with getting to your data faster. Snowflake already streamlines the path to get up and running with a simple sign-up process via our website. With the introduction of Partner Connect, getting started with loading your data just got easier.Featuring data integration partners Fivetran, Alooma and Stitch, Partner Connect accelerates the onboarding process for new customers by eliminating the time-consuming steps of signing up for a new partner application and manually configuring the connection to Snowflake. We want to remove all barriers to getting started so you can unlock the full value of Snowflake immediately. Partner Connect makes this possible.

Read the rest here: Get Started Faster with Snowflake Partner Connect | Snowflake Blog

Happy loading!

Kent

The Data Warrior

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