Snowflake Plugin Available NOW on VSCode

Snowflake Plugin Available NOW on VSCode:


We have great news! Snowflake has released its own VSCode Plugin! It’s currently in Public Preview (PuPr) and you can download it from the Microsoft Visual Studio Extension marketplace. With the Snowflake Plugin, you will have access to some features such as:


  • Accounts and Sessions: the plugin allows you to connect to and easily switch between multiple Snowflake accounts. And (this is cool) you can share a single session between multiple open VSCode editor windows! Support for Single Sign On (SSO) is available.
  • Snowflake SQL Intellisense: autocomplete for object names, keywords, and built-in functions, with signature help for function calls. Links to documentation for keywords and built-in functions on hover.
  • Database Explorer: a treeview-based panel that lets you drill down into object definitions and details.
  • Query Execution: not just single statements, but multiple statement executions!
  • Query Results and History panel: View and sort query results and export results to CSV format. Review prior statement history and results, and copy/paste support on previous queries.


How to install the Snowflake plugin on VSCode:


  1. Launch VSCode and head over to the Extensions Marketplace tab

     2. Type in “Snowflake” and select the verified Snowflake extension (It should have the verification checkmark)

     3. Click on the Snowflake icon to log in. The extension will ask for your account’s URL however this part can be tricky. Instead of inputting the whole URL just add the part before


For example, if your account URL is, enter in the Account Name/URL box.




    4. As a final step add your username and password and you are all set to go!


With these simple steps, you can now use the Snowflake Plugin on VSCode. If you want to learn about other new features on Snowflake, be sure to check out our blog for new updates.



The first snowflake summit finally happened from June 3rd to 6th and lived up to the expectation of many people who were interested in the summit. The four days summit had more than two thousand attendees, one hundred and twenty presentations across seven tracks, seven keynote presentations, more than thirty hands-on labs, more than thirty-five theatre sessions, and more than thirty countries represented by the attendees.

A quick recap of the summit…

Day 1

The first day of the summit majorly involved attendees of the summit undertaking an essential snowflake training which ended with the trainees taking an exam. This was a smooth and exciting experience as people were placed in rooms where they had their background scripts and environments with snowflake representatives ready to help anyone out. The exam was made of two parts, the first part was made of multiple choices relating to the training done, and the second part was done upon passing the first part, which was practical. The practical involved creating a user, a database, and a table that loaded from a Google spreadsheet, and executing various transformations that would load in the final table.

Day 2

The significant aspects of the day involved making important announcements about new snowflake features. The features included snowflake being available on Google cloud, external tables, snowflake organizations, data replication, data exchange, and data pipeline. The significant announcements are explained below:

  •      Snowflake announced that it would be available on the Google platform for 2020. This would ensure that organizations using snowflake get seamless and secure data integration across various platforms, thus enabling them to choose the right vendor for their business. It will also be easy for customers to utilize Google’s ecosystem of applications. Customers also can use the Google cloud platform and manage applications across multiple clouds.
  •      Snowflake also introduced new data pipeline features that allow customers to query data directly from their data lake on Azure Blob Storage or AWS S3 which enables them to maintain the data lake as the single source of truth.
  •      Snowflake’s data exchange is currently available for viewing privately with public viewing being set for later in the year. The data exchange is free to join marketplace for enabling users to connect with data providers for seamlessly discovering assessing and generating insights from the user’s data.

Day 3

The keynotes on the third day started with Alison Levine, who is the author of “on edge,” giving an informative talk on leadership. The founders of snowflake Benoît Dageville, who is the current president of products, and Thierry Cruanes, who is the current CTO, also gave a talk on the reason for starting snowflake. They did this by referencing their vision of; “Simply load and query data”. The day ended with Kevin O’Brien of and Julie Dodd of Parkinson’s UK showing how data could be used to make the world a better place.

Day 4

The last day of the summit saw Matthew Glickman, the Snowflake VP of Customer and Product Strategy, giving a closing keynote on some of its customer’s journey to be data-driven. Some of the customer representatives invited on stage included Brian Dumman, Chief Data and Analytics Officer, McKesson, Yaniv Bar-Dayan, Cofounder and CEO, Vulcan Cyber, and Michal Klos, Senior Director of Engineering, Indigo/Localytics. By the end of the summit, it was clear that the future of data had arrived with snowflake having the capability of providing trusted data solutions to its customers.

The 2020 summit will be better

The 2020 summit will be held on June 1st to 4th at the Aria Hotel in Las Vegas, which is a bigger venue. Considering the success of the snowflake 2019 summit, the 2020 summit will be more significant and will have more activities. I honestly can’t wait for it.

Find out more about all the benefits Snowflake has to offer you and your business. Sign up for a free proof of concept!

A New Era of Cloud Analytics


Hadoop was regarded as a revolutionary technology that would change data management and completely replace data warehousing. Such a statement is partly accurate but not entirely true since it has not been the case ever since cloud solutions came into the picture. Hadoop mostly flourishes with projects that involve substantial data infrastructure, meaning it was more relevant around a decade ago when most data analysts Continue reading

Not On The High Street: Improving customer experience with Snowflake

Not On The High Street: Improving customer experience with Snowflake

Companies like are taking their customers’ experience to the next level, with an online marketplace delivering unique products and services in a singularly convenient way. Without speedy data delivery though, as attested to by their Director of Data in this video, this marketplace just wouldn’t keep their customers coming back for more. Their countless partners benefit from this as well, but Continue reading

Snowflake vs Netezza

Snowflake vs Netezza

Fifteen years ago, IBM introduced an appliance-based, on-prem analytics solution known as Netezza. It was purpose built, load ready, and met a lot of the needs of the day (back when on-prem was still largely the preferred choice for data warehousing solutions). One could say IBM really hit the ball out of the park, and Netezza has definitely enjoyed a good, solid run since then, but that was fifteen years ago, and times have Continue reading

ETL vs ELT: Data Warehouses Evolved

ETL vs ELT: Data Warehouses Evolved

For years now, the process of migrating data into a data warehouse, whether it be an ongoing, repeated analytics pipeline, a one-time move into a new platform, or both, has consisted of a series of three steps, namely: Continue reading

Snowflake vs Hadoop

Snowflake vs Hadoop

Lots of people are aware of Hadoop for its advantages, like ease of data loading and scaling, but more and more are becoming increasingly aware of its limitations, like Continue reading

Snowflake vs Redshift

Intro – Snowflake vs. Redshift


When it comes to cloud data warehousing solutions, Snowflake and Redshift are two of the most popular options. Both solutions offer fast and efficient data processing, but there are some key differences to consider that can help determine which one is the better fit for your organization.

In this article, we’ll cover each one in-depth so you can decide what is the best option for you and your organization.




Snowflake is known for its ease of use and scalability, making it an attractive option for organizations that need a solution that is simple to set up and manage. Its cloud-native architecture allows it to automatically scale up or down as needed, which helps ensure that performance remains consistent, regardless of data size. Additionally, Snowflake’s user-friendly interface and support for ANSI SQL make it easy for teams to get started with the platform quickly.



Snowflake Benefits:


There are several benefits to using Snowflake Data Cloud, but the three most important ones are:

1. Ease of use and scalability

2. Cloud-native architecture that automatically scales up or down as needed

3. User-friendly interface and support for ANSI SQL

These features make Snowflake an attractive option for organizations that need a solution that is simple to set up and manage, with consistent performance, regardless of data size. Additionally, Snowflake’s user-friendly interface and support for ANSI SQL make it easy for teams to get started with the platform quickly.




On the other hand, Redshift is a popular choice for organizations that are already using Amazon Web Services (AWS). Redshift integrates seamlessly with other AWS services, which can make it easier to manage your entire AWS infrastructure from a single console. Additionally, Redshift’s pricing model can be more cost-effective than Snowflake’s, depending on the size of your data warehousing needs.


Redshift Benefits:


There are several benefits to using Snowflake Data Cloud, but the three most important ones are:

1. Seamless integration with other AWS services.

2. Potentially more cost-effective pricing, depending on data warehousing needs.

3. High performance for complex queries and large datasets.




It’s important to carefully evaluate the needs and priorities of your organization when choosing between Snowflake and Redshift. Consider factors such as ease of use, scalability, compatibility with other services, pricing, and any specific requirements your organization may have. By testing both solutions and comparing their features, you can choose the data warehousing solution that best meets your needs.

Ultimately, the choice between Snowflake and Redshift will depend on the specific needs of your organization, but with careful consideration and evaluation of each solution, you can make an informed decision that will benefit your organization in the long run.

Snowflake vs Teradata

Snowflake vs Teradata

To anyone with even a passing level of familiarity of this space, Teradata is quite rightly known as a powerhouse in the data warehousing and analytics arena. it’s been the go-to technology for sectors ranging from various 3 letter intelligence agencies, to the most recognizable of medicine, science, auto, and telecom industry players combined.

We have been building data systems for years, including those incorporating Teradata (such as for a Data Warehousing project we executed for TicketMaster back in 2007), so this really is the most excited we’ve been in all that time, given all the new capabilities available now, as compared to then. Today we wanted to share with you some findings based on several industry-leading reports concerning cloud data warehousing and analytics in 2017/18, especially as it concerns Snowflake when compared to such an industry giant such as Teradata. Continue reading

Strava: Data Sharing with Snowflake

Strava: Data Sharing with Snowflake

Data companies like Strava are really vertical pioneers, as they’ve created a veritable social network for athletes to upload, track, and compete with other athletes worldwide. As attested to by their data engineer in this video, without data, Strava wouldn’t exist, and the more people find they have access to it, the more they hunger for it. Yet as they grew, this imposed significant delays in the time it took for users to query their data, so beyond the data sharing features Snowflake uniquely provides, there were multiple benefits Strava encountered by using Snowflake. Continue reading

SpringServe: Data Sharing with Snowflake

SpringServe: Data Sharing with Snowflake

If ever there was an industry to discover huge benefits from Snowflake’s Data Sharing technology, it’s Advertising!

SpringServe delivers ads, in video format, with a reputation for providing immediate reporting on ad performance. They serve hundreds of thousands of ad requests per second, and as their collaborations and partnerships grew, so did the number of Continue reading

Localytics: Data Sharing with Snowflake

Localytics: Data Sharing with Snowflake

Localytics provides market engagement analysis services to makers of apps far and wide. Being a data company, and given the prevalence of mobile apps today, plus with how many clients were making use of their SDK, the scale of their data requirements climbed into the Petabytes. The costs for this level of data, using their legacy data warehousing system, shot into territory that just no longer made sense for them as a business (to say nothing for an ever growing latency issue as well). Continue reading

Playfab: Data Sharing with Snowflake

Playfab: Data Sharing with Snowflake

Here’s a great example of how Playfab, an online back-end service for game developers, leverages Snowflake’s Data Sharing functionality. Video games are increasingly delivered as part of an online service, and so game developers, now more than ever, are in need of one, secure space from which to host all their assets, tools, and data (both outbound, and from their players)! Continue reading

Semi-Structured Data Loading & Querying in Snowflake

Semi-Structured Data Loading & Querying in Snowflake

Unstructured data has become a tremendously popular format today (be it JSON, Avro, Parquet, or XML) for a multitude of things such as IoT, mobile, and apps. Until now though, there’s been no fast and easy way to store and analyze this kind of data, but Continue reading

Query Caching in Snowflake

Query Caching in Snowflake

Have you ever experienced slow query response times while waiting for a report that’s being viewed by multiple users and/or teams within your organization simultaneously?

This is a common issue in today’s data driven world; it’s called concurrency and it’s frustrating, usually delaying productivity just when the data being requested is needed the most. Well, here’s an incredible time saver you may not have yet heard about: Continue reading

Querying Multiple Databases in Snowflake

Continue reading

Snowflake Vertical: The Software Industry

How Snowflake has helped customers in the Software Industry

In this new series of articles, we’re going to be approaching things from a vertical perspective. It helps to narrow things down by industry sometimes, especially since, as a business leader, time is always of the essence. Let’s start with a couple of case studies, beginning with the software industry: Continue reading

Snowflake Vertical: The Service Industry

How Snowflake has helped customers in the Service Industry

This, our second entry in for the series on approaching things from a vertical perspective, is an obviously gigantic industry to try and cover, so let’s just look at a couple very different use case histories, from two totally different service industry enterprises: Continue reading

Snowflake Vertical: The Product Industry

How Snowflake has helped customers in the Product Industry

Nike ~ Sportswear

Nike is a sportswear company that probably needs no introduction. Continue reading

The Power of Instantaneous Data Sharing

The Power of Instantaneous Data Sharing – Updated

How awesome would it be to be able to share data more quickly instead of exporting it to some format like Excel and then emailing it out? I’m always looking for new ways to make sharing data faster and more easy. When I think back the past 20-30 years in tech I think of all sorts of data sharing tools and evolution of data. Do you remember VSAM files? Lotus Notes? SharePoint? Dropbox?

Find out more about all the benefits Snowflake has to offer you and your business. Sign up for a free proof of concept!

Frank Bell
July 27, 2018

(Continued… from

Over the past 20-30 years there have been tons and tons of investments made in BOTH people and technology in order to share data more effectively and quickly. We currently have millions of data analysts, data scientists, data engineers, data this and data that all over the world. Data is growing and growing and a huge part of our economy and our growth as a society. At the same time though the tools to share it never really were maturing that much until recently with Snowflake’s Data Sharing Functionality.

Before I explain how transformative this new “data sharing” or “logical data access” functionality is let’s take a step back and explain how “data sharing” worked before this.

Brief Tech History of Data Sharing. Here are some of the old and semi-new tools:

Good old fashioned physical media. (floppy disks, 3.5 inch disks, hard drives, USB drives, etc.)

Email. Probably still the best for smaller amounts of data and files. I’ve done it too. I need some super fast way to move a excel file with data from 1 computer to another fast. Email to the rescue.

SFTP/FTP. Secure File Transfer Protocol. File Transfer Protocol.

EDI (yuck) – Electronic Data Interchange – The business side of me has hives just thinking about expensive and crappy of a business solution this is. Companies spent millions creating EDI exchanges. The is a cumbersome and expensive process but at the time it was the accepted way to exchange data.

SCP. Secure Copy Protocol. Great command line tool for technical users.

APIs (Application Programming Interfaces). While APIs have been amazing and come a long way there still is technical friction with sharing data through them.

Dropbox, etc. Dropbox revolutionized the ease of sharing files mainly. It’s still not really great for true data sharing.

Airdrop type functionality.

Let’s face it though, most of these are primitive and have a lot of friction especially for non-technical users. Even when they are slick like Airdrop they typically don’t work across platforms and are often limited in data sizes and to discrete files. All of these solutions above have a lot of limitations when you think of the friction to get quality “data” and “information” for analysis and use from one place to another its still relatively painful.

Enter Snowflake’s data sharing. With Snowflake they have created a concept of “data sharing” through a “data share” which makes larger structured and unstructured data sharing a lot easier and one of the biggest improvements is there is only ONE SOURCE OF DATA. Let me say it again, yes, that’s ONE SOURCE OF DATA. This isn’t your typical copying of data which creates all sorts of problems with data integrity and data governance. It’s the same consistent data shared throughout your departments, organizations, or with customers.

The main point here is that there is true power in effective and fast data sharing. If you can make decisions faster than your competitors or you can help out your constituents with faster service than it makes your organization much better overall.

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Also, it’s just easy to do. With a very simple command you can share data to any other snowflake account. The only real catch is you do need a Snowflake account but this account you are only charged for what you use. For example, if you have a personal account that you don’t use very often then you are not charged anything per month except $40/TB of storage but if you don’t store anything you are not charged for that either and then the only charge would be compute (queries of someone else’s data share) which would be pretty inexpensive. For organizations with Big Data this cost is very reasonable compared to all the legacy solutions that were required in the past that are slower, more cumbersome, and more expensive.

What challenges does this solve today?

Cross Enterprise Sharing. (Let’s say you need to compare how different brands across websites are performing? Or you need to compare financials. You can easily share this data now with integrity across the enterprise and rollup and integrate different business units data as necessary.

Partner/Extranet Type Data Sharing. You can share data with much more speed and integrity with your partners with much less complexity than APIs require.

Data Provider Sharing. Data Providers that need to share data can reduce costs and friction by more easily sharing their data at the row level to different customers.

As things get more and more complex. (I mean is there really any corporation saving less data this year than last?) then we need to challenge ourselves to make things more simple. That is what Snowflake has done. I encourage you to take a look for yourself and try it out for free. We will be sending out some Data Sharing examples as well in the next few weeks so stay tuned.

Also, if you don’t believe me then look at all the reference case studies coming out in the last few months. Data Sharing has the power to transform companies, partners, and industries. Make sure you at least investigate it to make sure you are not left behind.

Here is a Data Sharing for Dummies Video for more information on the technology.

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Reference Case Studies: