Harnessing the immense power of the cloud with the Snowflake’s platform
When I was growing up, I watched Michael Porter, the most famous Strategic thinker of our times saying on television that firms could only pursue one of these two ways of competing: Low cost or differentiation. When I went to business school later, I was in awe of W. Chan Kim and Renée Mauborgne who had challenged Porter in their book Blue Ocean Strategy. It was about simultaneous pursuit of differentiation and low cost to create new markets using the ERRC (Eliminate, Reduce, Raise and Create) framework shown below:
After business school, when I joined the IT industry, I did not have the chance of working with or seeing closely a firm that effectively employed the Blue Ocean strategy. In 2015, Snowflake launched a cloud data platform for analytical and big data processing with a simple value proposition: Better performance at scale with significantly lower cost. How did they do it?
They eliminated the tight coupling of compute and storage for big data processing; raised the standards of performance at scale by leveraging the cloud’s unlimited resources; reduced the need for highly skilled professionals to write complex code to convert big data into value (through a familiar and easy to use lingua franca- the SQL) and in managing complex infrastructures (through self-provisioning and healing features); and created new features hitherto unknown in the industry like Secure Data Sharing and Data Marketplace. Snowflake created the network effect with the Snowflake Data Cloud, where organizations have seamless access to explore, share and unlock the true value of their data.
The result: Snowflake gained a huge momentum in the market and today they have about 4000 plus customers using Snowflake Data Cloud. With ease of use and lower costs, Snowflake has truly democratised data and made it possible for every organization- big or small- to be data driven.
As a Data Cloud, Snowflake can be considered analogues to a source of unlimited water that can be drawn out by anyone and one pays only to the extent of what is drawn out (consumption based pricing model). Some may use it for irrigation, some for generating electricity, some for providing clean drinking water, some to fight fires and so on. It is up to the drawer what he uses the water for and how efficiently he uses it to realize the maximum value of his investment.
Larsen and Toubro Infotech (LTI) is an elite Service partner of Snowflake. It has been responsible for implementing Snowflake across multiple customers and helping them migrate existing workloads to Snowflake. In doing so, LTI has combined its domain knowledge of working with these customers (for many years) along with Snowflake experience and expertise into a proprietary framework of processes, tools and accelerators to help its customers leverage maximum value from Snowflake investments.
A Framework for Maximizing Value from your Snowflake Investments
Snowflake provides enormous power in the hands of its users. In the past, big data analytical workloads would often be constrained by the ability to scale up compute and storage resources. Customers had to manage a fix amount of capacity. This is no longer the case now, as unlimited resources can be provisioned and made available in seconds with Snowflake. Accidentally deleting data was a nightmare in the past. With Snowflake features such as time travel, users of Snowflake can go back in history (up to 90 days) and can restore data that was corrupted or accidentally deleted. The power and capabilities in the hands of users increased more than a hundredfold after switching to Snowflake. With this power must come responsibility and accountability.
Moving from a capacity model to a cost-effective consumption model enforces a shift in the way the users interact with the system. Only limits now are customer imagination and their budget. At Larsen and Toubro Infotech, while working with customers during the early days of their snowflake journey, we have observed several scenarios where Snowflake was not being used in the optimal way. One of the customers was truncating and loading data into a database table every day while time travel was enabled for 90 days. What this means in crude terms is on every load the table is deleted and reloaded with all the data. This table alone occupied 90 times storage space as compared to its current size without having that requirement from the business. Another customer was running a query for several hours and the query failed at the end because of poor design. The query was consuming resources without providing any value.
LTI as a Global Service Partner for Snowflake has created the framework and tools to make sure that any dollar spent with Snowflake is aligned to business results. This is available through LTI Canvas Polar Sled FinOps. It integrates controls, best practices, Machine Learning, automation and cost mapping to get the most value for Snowflake investment.
At the very bottom of the pyramid is the setting up of controls to avoid unnecessary expenditures. This can be done by aligning and training users with how best to use snowflake according to the recommended practices prescribed by Snowflake and our known-how. For example, prior to migration to Snowflake for a workload- say loading data, we provide a cost calculator to calculate the annual compute credits for that workload as shown below. This is based on a simplistic assumption that a warehouse of size XS will run for 9 hours a day, 6 days a week and consume 2808 credits annually.
We then use the telemetry data provided by Snowflake and show the actual consumption as shown in the below diagram post migration
In this diagram, for the first nine months, i.e. from June 2020 to March 2021, the system is showing the actual consumption month on month. Against the initial estimate of 2800 credits for the year, about 2700 credits have already been consumed in the first ten months of usage. By deploying Machine learning on top of the actual consumption of the past period, we now help in forecasting the future consumption better instead of relying on a simplistic model of an initial estimate we created at the start. The future versions of Finops will also help in predicting Storage use similarly, particularly relevant when time travel is involved. This helps the users to be always conscious of their consumption- what they estimated at the start vs what it was later and how the system helps them make better forecasts for the future.
The next step in the pyramid is to just not be conscious of your usage, but also to be accountable for actions. Some organizations implement a charge back model to recover from individual departments /lines of businesses the cost of actual Snowflake usage. At this stage whether you chargeback or not, it is essential to ensure accountability amongst users organization wide. With thousands of queries running, understanding how the users are running those queries and identifying any outlier may save some unnecessary cost.
FinOps is able to automatically cluster users by their platform usage and identify areas that would need some investigation. In the example bellow, we can clearly identify the group of users that would need some analysis. Just clicking on any of those users FinOps will bring us to the root cause of those anomalies.
Further efficiencies can be squeezed out from the system by embracing automation to automatically implement control mechanisms without manual interventions. For example, in the above case, rules can be created to automatically disable the usage rights on a compute resource for a user or application without the account admin taking manual action to do so.
While the first three levels of the pyramid help in increasing the efficiency of your snowflake usage, the last level at the top is focused on effectiveness – i.e. focus on whether you are doing the right things. The ultimate measure of effectiveness is the value you are deriving versus the cost spent.
LTI customers are able to get insights of metrics like revenue per query when using FinOps. One of our manufacturing customers is using Snowflake to collect IOT data from all their devices. They also get all other product and customer related information existing in different systems into Snowflake. With all the information being in one platform, they can deploy Snowflake’s unlimited compute and storage capabilities to get insights from this data as quickly as possible. They can improve their product design and give the right advise to their customers based on usage observed across other customers. This helped them transform from being a product centric organization to a customer centric organization which led to increase in revenue and market share. The queries that they used to analyse this data directly resulted in top line and market share growth for the product.
LTI has combined all their experience and knowledge into LTI Canvas Polar Sled FinOps. At LTI we can provide better services by providing automation and Artificial Intelligence that translates in better quality at lower cost. If you are interested in knowing more, please talk to us.
The author Sumukh Guruprasad is a Snowflake Evangelist within LTI as well as across LTI’s customers and can be reached at Sumukh.firstname.lastname@example.org
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