Data analytics is the process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information which will support decision-making for organisations. Data analytics help a business optimise its performance, perform more efficiently, maximise profit, or make more strategically-guided decisions. Data analytics relies on a variety of software tools ranging from spreadsheets, data visualisation, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation.

What we offer!

Storilabs understands what is required for an organisation to become truly data-driven. Our data and visualisation engineers help clients to architect and build data platforms that simplify difficult challenges and provide a foundation to build on in the future. We are capable of choosing the right technology, tools and architecture for individual businesses. Thus the data is managed in a way that it is easy to make the right data-driven decisions.


ETL is a three-phase process where data is extracted, transformed and loaded into an output data container. The data can be extracted from one or more sources and it can also be output to one or more destinations. ETL uses a set of business rules to clean and organise raw data and prepare it for storage, data analytics, and machine learning (ML).

Data Warehouse

A data warehouse is a type of data management system that is designed to support business intelligence functionalities, especially analytics and reporting. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data (analytical data). Some of the cloud data warehouses we regularly work with include Google BigQuery, Snowflake, PostgreSQL, Amazon Redshift and Microsoft Azure SQL Data Warehouse.


Python offers several advantages for data engineering tasks. It helps data analysts to make sense of complicated data sets and make them easier to understand. Another pro of using Python is its high readability. Python code is easier for collaborating with other analysts, for communicating with other technical stakeholders, and it makes it more maintainable when it comes time to adapt it for new data sources and needs.


Google Looker Analytics and Business Intelligence Platform offers a powerful and scalable solution for data analytics and visualisation. It excels in data exploration, collaboration, and integration capabilities while providing a user-friendly interface.

Power BI

Microsoft Power BI streamlines data analysts' work and makes it easy to connect, transform and visualise data. Power BI can connect with 60+ popular solutions that nowadays companies often use. For example Spark, Hadoop, SAP. Users do not have to model data in their source systems, data can be integrated directly with the Power BI engine.


Snowflake is a fully-managed data service that's simple to use but can power a near-unlimited number of concurrent workloads. Snowflake Data Cloud platform delivers data warehousing, data lakes, data engineering, data science, data application development, and for securely sharing and consuming shared data.

Apache Spark

Apache Spark is an open-source, distributed processing system used for big data workloads. It utilises in-memory caching, and optimised query execution for fast analytic queries against data of any size. Built for performance, scale, and fault-tolerance, Spark enables teams to deliver on some of the most cutting-edge big data and AI use cases.

Apache Kafka

Kafka is one of the most popular and convenient tools available for stream analysis, Kafka is an open-source, distributed data streaming platform that is used for real-time streaming and microservice integration with platforms like Spark and Flink. Kafka is preferred for applications having online and offline analytics, anomaly detection, trend analysis, payment processing, infrastructure monitoring, and financial trading.


BigQuery is a fully managed enterprise data warehouse that helps businesses to manage and analyse their data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery's scalable, distributed analysis engine lets users query terabytes in seconds and petabytes in minutes.

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