Modern data warehouse
in Microsoft Azure cloud
A modern data warehouse provides users with instant access to analyses of all data from different databases and systems. The analyses can be extended to include Big Data as well as the creation of machine learning models. An important element is easy operationalisation of these analyses.
A unified environment significantly reduces the time required for analytical projects. It allows for a comprehensive development of various analytical solutions.
All stakeholders can gain instant insight into the company’s operations, using the latest available data from the deployed systems at any time.
Another important aspect is data security. Unsurpassed security measures protect data. We offer the most advanced security and privacy protection functions, such as column- and row-level security measures and dynamic data masking.
Who is a modern analytical environment for?
The modern analytical environment in the cloud is addressed to different target groups:
Find out the details and benefits of this solution
We understand our clients and know how difficult it is to make the transition to modern data analysis. The challenges we face are related to the complexity of environments, large amount of data (Big Data), diversity of data, variability of data over time, cost of environments, as well as insufficient knowledge and availability of people in the team. This is why we propose an iterative approach. We work in an agile way and adapt our services to your business needs. We conduct the implementation project according to the Microsoft Analytics on Azure methodology. What is important for us is demonstrating the benefits of the implementation from the very beginning of the project execution.
We start the cooperation with a pilot project. We prepare the client’s team for the implementation of a modern analytical platform – data warehouse – in the Microsoft Azure cloud. The most important goal of the project is to build the team’s competencies so they can understand the modern analytical environment in the Azure cloud and develop it further. The key points of the project are analytical workshop meetings whose aim is to gather infrastructure, data and architecture requirements. The next step is to develop a concept for working with data by identifying roles, processes, rules and defining the lifecycle of the environment. We then develop competency profiles and propose paths of future development. The project ends with a workshop demonstrating the functions of the technologies used in the architecture. We present the whole process in the workshop: data collection, data transformation, data model, data mining, reports, predictive analyses, cockpits, sharing of all analyses and teamwork.
When we talk to clients, the most common challenges we encounter are those that affect the entire organisation, the processes of data collection, storage and reporting:
- Organisations work in data silos that are placed in different systems, so it is difficult to report on them. Multiple analytical solutions are applied, some initiatives are repeated but we expect one consistent solution.
- The cost of storing historical data is too high. However, we do not want to give up data collection because it could be useful in the future for in-depth historical analyses. Reports are static, there is a lack of interactivity, they could be accessible online and in mobile applications.
- We have a lot of different reports, they present different data, there is chaos, people lose confidence in the data presented, and we have a problem creating a comprehensive and up-to-date map of the analytical environment.
- We are not able to classify data, divide it according to the degree of confidentiality and relevance to the organisation, assign business responsibility, trace the origin of the data.
- Reports are created by technically-oriented people, business employees cannot create reports themselves.
- We have a problem combining relational data with unstructured data for advanced analysis.
- Our analytical environment does not have adequate computing power and reports run slowly. Nevertheless, we do not want to scale up the environment and pay for additional licences.
- Managing the entire analytical environment is challenging for us and requires various competencies.
- There are a lot of integration systems from different vendors, they need to be maintained, competencies of team must be built, which generates costs. The policy of granting rights is not entirely clear, we are not sure if everything is well secured.
Key characteristics of the service:
- Unified analytical platform – data integration, data mining, data warehousing, big data analysis, machine learning in a single, unified environment.
- Analytical environment as a service – you do not need local servers to benefit from a powerful and secure environment. You can handle large volumes of data by building a so-called data lake, choosing the most cost-effective price options for each workload.
- Storing and exploring of diverse data – you can create a data store which is relevant for your business, and a combination of relational and non-relational data. Easily perform queries concerning files stored in the repository by using the same service that is used to create data warehousing solutions.
- Hybrid data integration without code – ETL and ELT processes are handled in a visual environment without the use of code. You can use over 95 native connectors to different systems.
- Environment for data scientists – provides deep integration with Apache Spark and SQL. Improve collaboration between data scientists working with advanced analytical solutions. Easy-to-use T-SQL queries in the data warehouse and Spark.
- Cloud-native hybrid transactional/analytical processing – you can easily extract detailed information from transactional data stored in operational databases such as Azure Cosmos DB in real time.
- Choice of preferred programming language – different programming languages, for example T-SQL, Python, Scala, Spark SQL and .Net are available.
- Use of artificial intelligence and business analytics – you can build end-to-end analytical solutions through tight integration with Azure Machine Learning, Azure Cognitive Services and Power BI.
- Comprehensive management, monitoring and security – work automation function allows you to simplify monotonous tasks related to the day-to-day administrative work connected with data monitoring and security.
By implementing modern cloud analytics in your organisation, you choose to improve business performance and process efficiency, and build a data-driven organisational culture. You reduce the cost of storing and sharing a variety of data. Users work with greater satisfaction, and get new opportunities to present data as attractive reports and manager dashboards.
You build competitive advantage through faster access to reliable data, reduced time-to-market, better customer service, and increased income. New opportunities arising from the combination of relational and unstructured data provide the opportunity to launch new services or create new products.
- Azure Data Lake
- Azure Data Factory
- Azure Synapse Analytics
- Azure SQL
- Azure Cosmos DB
- Azure Analysis Services
- Azure Purview
- Azure Maps
- Azure Databricks
- Azure Machine Learning
- Microsoft Power Apps
- Microsoft Power Automate
- Microsoft Power BI
- Azure Data Share