Data Hubs On NoSQL DBs + SlamData Are A Breakthrough In Enterprise Analytics
Build A Data Hub On MongoDB, MarkLogic, Couchbase Or Hadoop In Minutes Using SlamData
Data Diversity Welcome!
Have customer data in your CRM, ERP system, transactions in your ecommerce platform, clickstream data…? It literally can go on and on and on. You can now push all your disparate data into one place and SlamData will read it — it’s the tool that finally gives you insight on live data — event super complex, nested and messy data.
Explore, Visualize and Share/Embed
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 dashboard.
Our security model supports multi-tenant security. The flexibility to serve your customers is unprecedented.
We “push down” analytics to your data, that means computation is in your database. What does that mean? SlamData scales along with your infrastructure. It’s lightweight.
Works On These Leading NoSQL Databases
- A leading company in the developer bootcamp space.
- They are working with over 10 large universities as the in-house provider of ongoing/continuing education.
- The company built an app on MongoDB to track student performance and manage attendance, homework and other attributes.
- They tried building a bespoke reporting solution. It didn’t work.
- They tried a number of ETL-oriented charting tools. That didn’t work.
- The company also had a marketing solution that had its own native reporting.
- The company couldn’t find a way to mash data together and build reporting solutions for each department.
- The lack of insight was a drag on their growth.
- The SlamData Client Solutions team assessed their needs quickly and determined that using MongoDB + SlamData as a Data Hub (or fast-to-assemble data warehouse) would give them consolidated reporting without any data preparation.
- With SlamData as their reporting engine, they can do custom, live reporting off of MongoDB.
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
Who Is Using SlamData?
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