Big Data & Analytics

Convert complex data into meaningful information.

Improve thinking and decision-making with analysis of raw data

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.

Image of three analytics platforms using big data and data analytics

Some examples of some of the multiple applications for big data and analytics include:

  • Detailed analytical data on consumption, water supply and monitor the loss of water.
  • Protect resources and prevent fraud.
  • Identify leaks, disruptions, or other inefficiencies in the water system.
  • Early warning systems for disasters.
  • Calculate harvest yields, fertiliser demands, schedule irrigation, identify optimisation strategies for future crops and mitigate environmental pollution, all of which can reduce costs.
  • Smart cities can gain insights from big data collected through various sources; the Internet of Things (IoT) allows the integration of sensors, radio-frequency identification, and Bluetooth through networked services.
  • Track river and estuary ecosystems with sensors, robotics and computational technology to monitor water quality.
  • Big data techniques have been successfully used for different applications as different as food security, oil spill detection and urban planning.

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.


  • Ability to store and process both structured and unstructured data: Data lakes rely on object storage and Hadoop ecosystem tools to store and process all kinds of data.
  • Data lakes support the same data visualisation, data reporting and business intelligence workflows but also new artificial intelligence workflows, enabling applications related to Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) to name a few.
  • Use cheaper storage - data lakes use object storage, the most affordable storage available.
  • Data lakes collect and process data in batches, as well as streams of data.
  • Can scale to a cluster of nodes: Data lake tools were designed and developed with Massive Parallel Processing in mind, facilitating optimisations such as the ability to work with compressed and columnar formats.

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