Prototyping, ad hoc and research analytics
Need quick answers to get to an important decision or for a prototype?
Crunch large datasets for research ?
RAW NoDB is your fastest path to the answers you need.
You need to rapidly prototoype product ideas, or get fast answers to complex and/or ad hoc questions. The data is spread among different databases, formats and files some of which maybe outside your organization. Getting to answers often means the well-trodden path: access and profiling the datasets, a knowledge of how to query, what to extract, transform and then load into a target – before then integrating the data for the answers to be available. To do this, data landing space and structures need to be created, along with mappings, conversions, load scripts and many queries, and often external functions are brought in to support this method where the databases are lacking.
All this takes time away from the main goal which is asking questions, getting answers, and then iterating swiftly.
New ideas are slower to develop, the innovation culture is stifled as users wait for multiple teams that can use these multiple tools to set up their environment for the tests, write and provision the code to extract, transform, load the data, integrate and produce the answers. Time and money is wasted waiting for large amounts of data to be transferred from one store to another or downloaded from external sites and landed to internal repositories, before it is even known if this data is genuinely useful.
Our RAW SQL allows files, databases and web services to be queried together like a database and output directly in any format with a single SQL-like functional language. External functions are integrated directly inside the RAW code, meaning a single environment for your prototypes, ad hoc questions or research analytics.
Our NoDB approach accesses data at source, so with no copies of data needed you can get asking questions immediately. No ETL code, no mappings, structures to create, just start asking questions and getting answers immediately. RAW NoDB caches from behavioural patterns based on queries and in a highly iterative environment this saves time accessing data repetitively.
USE CASERapid protoyping
Providing data for protoyping needs a highly iterative process where data can be explored, formats changed or added, with new data often located in a variety of locations. Doing this the traditional way has meant multiple tools, queries and scripts, and copying the data from those sources which all increases time and effort.
RAW allows you to very quickly start asking questions without copying and landing data. Query all your data sources with a single interface and an easy to use SQL-like language and produce flexible outputs. Prototypes are hence much faster to create, with shorter iteration cycles.
USE CASEResearch analytics
You have a new idea and require new and existing sources of data to explore and validate. Often the datasets are very large and complex, copying and transforming may take more time than asking the questions, or the questions are very complex across many sources so the data needs to be landed in one database where you know the query language well.
With RAW there’s no copying of external data required, and Query-as-a-Service means you can just ask questions at source – including querying a web services result-set in JSON directly like a database. Ask a similar question the second time and data is automatically cached. And with only one language to use there’s no scripting, glue-code or various dialects of query languages to know.
With RAW you can deliver rapid prototypes, ad hoc or research analytics, incredibly fast, here’s why:
- Data polyglot, connect to any data source and output in any format.
- Single query language, providing one interface to reduce extra code and reduce burden of querying many sources
- SQL data views and API’s provide multiple ways to consume output
- “NoDB” approach serves data without unnecessary data copy and hence database structures
- Smart caching, learns from what you’ve queried so that the next results are faster