5 Distinctive GoodData Use Circumstances for Snowflake Information Customers

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There are a plethora of instruments and platforms to select from on the subject of constructing  dashboards with Snowflake information. For constructing interactive analytics apps with Snowflake, there may be GoodData.

GoodData and Snowflake make a wonderful mixture for working your analytics app. Your subsequent query is why, proper? The reply is a bit long-winded however learn on to study in regards to the 5 distinctive use instances GoodData gives to help Snowflake information customers.

1. Eradicate Change-request Overload

The State of affairs

In analytics, one dimension doesn’t match all. Finish customers will all the time be in search of one thing straight suited to their wants (i.e., a unique view of the information). This results in your staff will rapidly develop into inundated with customization requests.

GoodData Answer

That is the place multi-tenant structure, a well known GoodData staple, turns into a necessity. By offering separate workspaces — devoted areas the place customers can analyze their information and consider their dashboards — for every consumer firm or person group, you possibly can simply allow end-user customizations of dashboards and studies whereas making certain that every group’s information is separate and safe. On high of this, with plans priced per workspace slightly than per person and the flexibleness so as to add limitless customers per workspace, you possibly can rapidly and simply scale your product alongside along with your Snowflake information warehouse.

2. Scale Analytics Alongside Snowflake Information Storage With out Sacrificing Efficiency

The State of affairs

Whether or not you propose to roll out analytics internally to workers or externally to prospects, one of many principal targets on your analytics answer will probably be to supply analytics to as a lot of your finish customers as attainable. Nevertheless, the flipside to that is that as your end-user uptake will increase, so do the efficiency necessities of your information storage and your analytics. As well as, profitable analytics functions are fairly taxing from an operational perspective. As your software positive factors traction, you’ll quickly see information volumes and concurrent person numbers develop, together with the prevalence of peak utilization occasions.

GoodData Answer

On this occasion, elastically scalable analytics is required to enhance your Snowflake information warehouse. GoodData’s elastic scalability effectively scales by information quantity, person quantity, and price; in order your Snowflake information storage grows, your analytics and person numbers can scale together with it — with out sacrificing efficiency.

3. Leverage Reusable Metrics to Empower Finish Customers

The State of affairs

Whereas multi-tenant structure is one main requirement for offering self-service analytics, one other problem is knowing who your finish customers might be. They probably received’t all be analysts by career, which is why each step in the direction of ease of customization is efficacious. It additional helps to stop customization requests that may in any other case go to your product, help, or skilled companies groups.

GoodData Answer

GoodData’s answer is to implement reusable metrics. Reusable metrics is the simplest solution to obtain ease of customization. By making a semantic mannequin and defining base metrics that your finish customers can later use when creating their particular metrics as easy arithmetic expressions, your finish customers can handle their analytics effectively and confidently.

Data model example
Outline base metrics your finish customers can reuse.
Logical data model with stacks of technical and business metrics
Obtain ease of customization with reusable metrics.

4. Eradicate Information Silos and the Must Transfer Information

The State of affairs

With information being collected from a number of sources and moved between departments and functions, the prevalence of information silos and rancid information is a standard drawback for corporations rolling out analytics.

GoodData Answer

Your Snowflake information warehouse solves a part of the equation by offering one location for storing all your information from scattered information sources. The opposite half of the equation? GoodData Cloud to straight question your Snowflake information in actual time for all the time up-to-date information analytics — with out the necessity to transfer information whereas additionally eliminating information silos.

5. Keep away from Metrics Inconsistencies

The State of affairs

As described above, with an analytics answer straight querying your Snowflake information in actual time, finish customers all the time have entry to the freshest information. On the similar time, you keep away from the necessity to transfer information. Nevertheless, a profitable analytics software will probably contain a variety of customers, analysts, builders, and information scientists who received’t be happy with simply interactive information visualizations and dashboards.

They’ll need to use the analytics ends in a number of different functions (e.g., BI instruments, ML/AI notebooks, and so on.) that type a part of their workflow and mix these leveraged metrics with their queries. As a substitute of counting on outdated information exports, they’ll need to hook up with the semantic layer and get real-time metrics, reminiscent of utilizing their Python code with GoodData Python SDK.

Many corporations strategy this want through the use of a number of instruments and platforms that sit on high of a shared database. Nevertheless, making certain analytics consistency throughout these varied instruments is tough as a result of every software can use a unique information mannequin and question language in addition to snapshots of information from completely different occasions. All of those variations may cause customers to make use of ungoverned calculations of their instruments. Unsurprisingly, this results in information inconsistencies when 4 customers report 4 completely different values of the identical KPI.

GoodData Answer

Right here is the place headless BI is the answer. Headless BI permits finish customers to attach on to the analytics engine embedded in your functions by way of commonplace APIs and protocols (e.g., JDBC or ODBC) to supply up-to-date, clearly outlined information.

Headless BI schema
Guarantee constant analytics outcomes with headless BI.

Strive GoodData + Snowflake

Need to study extra about how you can get essentially the most out of your Snowflake information with GoodData? Learn extra about the advantages of our technical partnership or request a demo right now and we’ll provide you with an in-depth guided tour.

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