Managing critical water assets
Cost-efficient water-cycle services
Awareness & understanding
Increase food production
Welfare & healthy growth
Ensuring safe water
Access to timely water data
Reliable observation and forecasting
Sustainable water infrastructures
Efficient and competitive
Support, prevent and improve
Innovative, fast and low risk
Cost effective and scalable
Convert complex data into meaningful information.
Modern analytical data platforms offer much more than classical data warehouses can handle; transaction support, scalable metadata handling, streaming and batch unification and schema enforcement.
ADASA’s state-of-the-art data platforms can meet a broader range of analytical requirements and workloads, such as real-time analytics, SQL, data science and more. Storage is decoupled from computing, allowing better scalability, and open storage formats enable easy access to data via interfaces (APIs). Security, access control, data governance and data discovery are all viable functions, too.
The cloud provides the environment for these data platforms, offering performance, scalability and reliability as well as a diverse set of analytic engines and massive economies of scale. The cloud also brings better security, faster deployment times, frequent feature and functionality updates, more elasticity and lower costs linked to actual usage.
With the increase in amounts of miscellaneous data and the number of data users, our analytics platforms meet and surpass data analytics and data science requirements – we equip teams with different tools to meet their performance needs. This is achieved with a data lake.
Some examples of some of the multiple applications for big data and analytics include:
All types of data can be added via batch and streaming sources, and sorted in its purest, raw format, be it structured or unstructured data. It is then loaded into the data lake and transformed in multiple ways when required.
Powerful data pipeline tools allow authorised users, such as data engineers and data scientists, to write, deploy and monitor data workflows. Data visualisation and data dissemination tools, including dashboards, automated reports and REST APIs, present the information in useful and meaningful ways. People can share logic and findings through interactive notebooks (e.g. Jupyter notebooks).
Data science tools can train, evaluate, deploy and monitor machine learning and deep learning models using specialised frameworks. Geospatial tools work alongside spatial data to efficiently process geographical operations, too.
Data makes make it possible to blend cheap cloud storage with the computing power necessary to meet today and tomorrow’s requirements.
Assuring dam safety through high quality and timely data and analytics.
Building customer confidence through insight into decision making.
National Water Information System
Ministry of Environment of Spain
Improving national water policy and transparency through standardised, managed and accessible data.