It’s easy – connect GeoSpock DB with data analytics tools you already use – or use GeoSpock DB to power new applications
Connect GeoSpock DB to any BI tools used by your team, including Tableau, Power BI, QuickSight, and more! Get the power you need from GeoSpock DB through tools you already love.
Use GeoSpock DB to power data science tools like Jupyter Notebooks for Python and Zeppelin Notebooks for Apache Spark. Surf your data on the fly, without worrying about waiting for hours to get the answers to your data questions.
Bring your data to life by integrating GeoSpock DB with libraries to help visualise your data. Present and explore data intuitively using maps, models, and visual interfaces powered by GeoSpock DB to show the real-world context behind data-driven decisions.
GeoSpock DB supports the ingestion of datasets in the following formats, with or without compression:
For more information about preparing your data for ingestion into GeoSpock DB, refer to our documentation.
Traditional database technologies were designed to run on single, very powerful machines. As the availability of cheap cloud computing has become widespread, a new generation of ‘big data’ databases has emerged which can be designed to ‘scale-out’ across multiple compute nodes, with more compute and storage capacity being added for each additional machine.
However, many of these systems suffer from centralised bottlenecks and diminishing returns as both complexity of analytics and size of data stored increase. GeoSpock DB’s approach solves these fundamental issues by fully separating storage and compute, removing bottlenecks and ensuring that the system has true linear-scaling properties. This also ensures that both compute and storage can be right-sized for any application or any scale of problem-solving.
Almost all enterprise data analytics platforms suffer from data silos and a lack of flexibility. Often, each new application requires a new database instance and a separate data copy to be made, massively increasing storage costs, making the whole system complex and introducing issues such as synchronisation overheads. This combination of added cost and complexity creates barriers to new application development, discourages new innovation and limits the value potential of data within your organisation.
GeoSpock DB follows a concept of dynamic data fusion – a new design philosophy which follows a single-instance-many-applications approach to database design. Dynamic data fusion enables a plug-and-play approach to data and insights-exploration. Datasets are kept logically separated and dynamically fused together at the point of query. This major step forward in database design eliminates the need for multiple copies of data, dramatically reduces storage costs, and enables an agile approach to data-powered product development.
GeoSpock DB is designed for the data-driven future. As more businesses use digital insights and increasingly adopt a connected-everything mindset, ever-larger quantities of data are being produced. To overcome the challenges of volume, variety, and velocity in big data management and analytics, existing solutions have resorted to brute force techniques and expensive storage solutions. This may work initially, but ultimately leads to unpredictable and spiraling database costs.
GeoSpock DB breaks this spiral. Our system scales linearly in the face of increasing data volumes or query complexity. Combined with low-cost cloud storage, GeoSpock’s scale-on-demand, high-performance query engine not only minimises total cost, but also provides you with predictability and control over the cost of your own data management systems.
Existing database solutions require careful configuration and tweaking for each individual use case or application. Though accessible through industry standard ANSI SQL, GeoSpock DB has a unique machine learning-powered indexing and query engine which automatically self-optimises internal data structures and storage mechanisms. This enables high-performance queries with none of the usual management overheads of traditional systems.
This allows developers to focus on building the core value of new applications, rather than on ongoing maintenance and system administration.