Looker

Intro - What is Looker?

Looker is a business intelligence software and big data analytics platform that is designed to analyze both structured and semi-structured data. It is ideal to use with Snowflake, as it allows users to analyze data from disparate sources, transform data into knowledge right at query time, and leverage all the power and flexibility of Snowflake directly from Looker. Additionally, it enables users to make sense of all their data with one tool and to chart, graph, format, and explore data in one place. It also allows users to share data via URL, access data wherever they need it, schedule delivery, and export data locally or directly to Google Drive, Dropbox, or S3. Looker is fully embeddable with SSO and has a responsive mobile design.

Looker is the ideal tool to use with Snowflake:

  • Analyze both structured and semi-structured data with ease.
  • Leave your data in Snowflake, granularly control access from Looker.
  • Define business logic to transform data into knowledge right at query time.
  • Any data that is shared through the Sharehouse is immediately available in Looker. Known datasets can use Looker Blocks™ and extensions to share a data model as well as data.
  • Snowflake’s advanced SQL dialect is rich in features. Leverage all the power and flexibility of Snowflake directly from Looker.

Analyze data from disparate sources:

  • Adapt Looker to fit your data.
  • Direct connection to any SQL database.
  • Virtual, code-based schema applied at query time for flexibility.
  • Make sense of all your data with one tool.

Governance:

  • Centrally-defined, version-controlled business logic lives for everyone to use.
  • Collaboration and versioning with Git.
  • Every analyst gets a sandbox.
  • Every version of your definitions is easily accessible and implementable.

Give everyone immediate access to information:

  • Everyone can self-serve from curated data.
  • Connect directly to the database to explore all your data.
  • Centrally defined and shared metrics.

Chart, graph, format, and explore data in one place:

  • Web-native, interactive visualizations.
  • In-browser visualizations.
  • Drag & drop to create or edit their dashboards.
  • Custom visualizations.

Leverage the Value of Your Data:

  • Sharing via URL.
  • Access your data wherever you need it (Slack, Salesforce, etc.).
  • 100% browser-based.
  • Everyone can schedule delivery.
  • Export locally or export directly to Google Drive, Dropbox, or S3.
  • Fully embeddable with SSO.
  • Responsive mobile design.

Conclusion:

In conclusion, Looker is a powerful business intelligence software that is designed to analyze both structured and semi-structured data. It is particularly useful when used with Snowflake, as it enables users to leverage all the power and flexibility of Snowflake directly from Looker. With its ability to analyze data from disparate sources, transform data into knowledge right at query time, and explore and share data in one place, Looker is an ideal tool for businesses looking to make the most of their data.

If you want more news and information on Snowflake updates, be sure to check out our blog.

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: (more…)

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 (more…)

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:

 

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.

 

Redshift:

 

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.

 

Conclusion:

 

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. (more…)

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. (more…)

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 (more…)

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). (more…)

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)! (more…)

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 (more…)

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: (more…)

Querying Multiple Databases in Snowflake

(more…)

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: (more…)

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: (more…)

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. (more…)

The Data Warehouse Evolution

Data Warehousing has been around for quite a few years. The last 20 years it has made the greatest impact on businesses. With the recent exponential growth in customer and sensor data then its been challenging for traditional data warehouses and appliances because they just were not designed for that amount of data. The cloud data warehouse has evolved to solve that and enable businesses to be more data-driven.

Our big data practice has always sought to find the best solutions for our customers and we have had to evolve as the data collection and the data warehouse has evolved over the last 20 years. We have gone from RDBMS (Oracle, SQL Server, MySQL) solutions to Appliance (Teradata, Netezza, Vertica) to Hadoop (HDFS/Hive, etc.) to Cloud (Redshift, Azure, BigQuery) to finally now.... A fully Elastic Cloud Data Warehouse that was designed from the ground up to leverage the cloud's scalability (Snowflake). (more…)

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 https://www.linkedin.com/pulse/power-instantaneous-data-sharing-frank-bell/)

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:

Playfab

Localytics

SpringServe

Strava