Frequently Asked Questions
How is it possible that SlamData does not require ETL or mapping semistructured arrays to relational data models?
Our analytics super compiler leverages advanced multidimensional relational algebra which allows standard SQL queries. Our technology also dynamically adjusts when new fields, arrays and subdocuments are noticed. This all happens live, with no administrator intervention necessary.
What are the system requirements for SlamData?
We recommend running SlamData within a virtual machine of some type where the administrator can increase the memory and CPU speed and number of cores when necessary. Development environments typically consist of a dual or quad core system with 4GB of memory, with at least 2GB reserved for the JVM. Larger production environments should start with quad cores and 6 – 8 GB of memory, with at least 4GB of memory reserved for the JVM. SlamData requires approximately 500MB of installation space. SlamData stores it’s analytical workflows in the target data source itself, so local hard drive space is not needed as the number of workflows grow.
Where can I run SlamData?
SlamData runs within a JVM so it can run on Linux, Windows and macOS. SlamData can run in a virtual server, a physical bare-metal server and laptops. It has been successfully deployed with Docker, VMware, Oracle VirtualBox and other container technologies.
How fast is SlamData?
SlamData pushes 100% of the computation down to the target data source so we limit both the amount of data we perform calculations on as well as the amount of data going over the network. Every other solution available sends simple queries to a database and pulls back an unnecessarily large data set to computations against; SlamData sends an optimized, type-safe query to the target data source and forces the target data source to perform the calculations. So in essence, SlamData runs as fast as your target data source can run the query. SlamData is not a bottleneck in the analytics process.
Why would I choose SlamData over other solutions?
Other solutions do not provide fully embeddable workflows, SQL on NoSQL data models, live access to any and all data source targets (relational and non-relational), ability to host your own SaaS version of analytics based on per-customer non-materialized views or OAuth2 / OIDC security, auditing, all in one easy-to-use and simple installable package.
Do you offer commercial support for SlamData?
Yes, we do. The Advanced Edition comes with Support where businesses have direct access to SlamData experts.
Do you offer consulting or other services?
Yes, we do, on a limited basis. We offer a JumpStart program which helps get businesses off to a great start with a Proof of Concept (POC) or Minimum Viable Product (MVP) in a very short time.
Who Is Using SlamData?
The Characteristics of NoSQL Analytics Systems
- 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
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