Understand Everything Happening In Your SaaS App. Right Now. Visualize, Share and Embed the Insight.
Understand Your Business Right Now
Once you connect SlamData to your data source you can start querying, visualizing and understanding your data and your users. We’re talking minutes, not days or hours.
We’re NOT talking canned reports. SlamData let’s you pull off the most complex query. That’s just the beginning.
Embed Charts and Full-Blown Interactive Dashboards. For Your Team or Your Customers.
Once you find the data you’re looking for — or the chart that explains it all — you can share that data or embed it into an intranet or external customer-accessible dashboards. Our security model enables multi-tenant security. That will turbo-charge your app — and your business.
We push down analytics to your data so SlamData scales along with your infrastructure. No additional devops, no additional overhead like your traditional data warehouses or ETL-based analytics solution.
- A startup focused on helping kids get paying gigs.
- MVP is live and they’re adding more and more users. Traction is happening — the team now needs high-quality so they can scale.
- They built a MEAN stack app. Development was fast. But analytics came up short.
- They knew the metrics they needed to track, but pulling together persistent reporting — charts and dashboards — wasn’t possible.
- That’s then they searched the web for “real-time analytics and dashboards for MongoDB”.
- The SlamData Client Solutions team assessed their needs and quickly determined that adding SlamData to their stack would give them all the insight they needed within a few hours.
- Embedding their KPIs into their intranet was icing on the cake.
- SlamData become the right solution: fast, flexible and easy to share.
What’s Your App Built On? More Coming Soon!
SlamData allows us to connect directly to collections in our MongoDB database without the need for special drivers or other connectivity software.
Ken - Database Engineer
Whitepaper: The Characteristics of NoSQL Analytics Systems
by John De Goes, CTO and Co-Founder of SlamData
Semistructured data, called NoSQL data in this paper, is growing at an unprecedented rate. This growth is fueled, in part, by the proliferation of web and mobile applications, APIs, event-oriented data, sensor data, machine learning, and the Internet of Things, all of which are disproportionately powered by NoSQL technologies and data models.
This paper carves out a single concern, by focusing on the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. This approach leads to eight well-defined, objective characteristics, which collectively form a precise capabilities-based definition of a NoSQL analytics system.
These capabilities are inextricably motivated by use cases, but other considerations are explicitly ignored. They are ignored not because they are unimportant (quite the contrary), but because they are orthogonal to the raw capabilities a system must possess to be capable of deriving analytic value from NoSQL data.
Table of Contents
- The Nature of NoSQL Data
- NoSQL Databases
- Big Data
- A Generic Data Model for NoSQL
- Approaches to NoSQL Analytics
- Coding & ETL
- Real-Time Analytics
- Relational Model Virtualization
- First-Class NoSQL Analytics
- Characteristics of NoSQL Analytics Systems
- Generic Data Model
- Isomorphic Data Model
- Unified Schema/Data
- Polymorphic Queries
- Dynamic Type Discovery & Conversion
- Structural Patterns
Who Is Using SlamData?