Transparity’s data engineering consultancy experts can create you a modern data platform, to manage your data, inform your decisions and accelerate your growth
Through Transparity’s data engineering consulting, our specialists design and implement a tailored modern data platform for your organisation – resolving not just your initial challenges but creating scalable cloud-native architecture that won’t fall victim to disruption in the future.
Transparity, as a trusted data engineering partner, offers data engineering & cloud migration services and data engineering solutions with a direct relationship to Microsoft, leveraging funding to aid you in your data projects.
With Transparity’s data engineering solutions and the right modern data platform, you no longer need to devote large portions of time to data input and transfer. This gives you back valuable time to focus on interpreting and analysing data and boosting overall efficiency for teams and the business.
Transparity’s data engineering services, enhanced by our data engineering & cloud migration services, resolve some of these typical problems:
Overcomplicated processes that drive business decisions
Poor customer service or experience
Difficulty in forecasting future trends
Fraud, data leakage and loss
Siloed systems and duplicate data
Difficulty in making data-driven decisions
Hard to understand the ROI metrics
Lack of integration with emerging technologies
Implementing a modern data platform with Transparity’s data engineering solutions involves deploying advanced technologies and methodologies to process, manage, analyse, and visualise large volumes of data efficiently. By adopting a modern data platform with Transparity, your organisation can gain valuable insights, improve decision-making processes, and drive innovation.
Data ingestion involves the process of collecting and importing data from various sources into a centralised data repository. These sources can range from traditional databases, cloud storage, IoT devices, social media platforms, and more. Effective data ingestion tools ensure that data is gathered in real-time or batch mode, preserving data integrity and preparing it for further processing and analysis.
Data storage solutions provide the backbone for storing vast amounts of structured and unstructured data. Modern data platforms leverage cloud-based storage options such as data lakes and data warehouses, which offer scalable and flexible storage capacities. These solutions not only store data efficiently but also ensure its security and availability, enabling organisations to access and use their data whenever needed.
Data processing engines are responsible for transforming raw data into meaningful insights. Tools like Apache Spark facilitate large-scale data processing by performing complex computations quickly and efficiently. This stage includes data cleaning, transformation, and integration, making it possible to analyse data and extract valuable insights that drive business decisions.
Data management encompasses the policies, procedures, and technologies used to ensure the accuracy, quality, and security of data throughout its lifecycle. Effective data management practices include data governance, which establishes data ownership and accountability, as well as data quality measures that ensure data is consistent and reliable. Security protocols protect data from unauthorized access and breaches.
Data analytics tools enable organisations to query, analyse, and visualize data to uncover patterns, trends, and insights. Platforms like Azure Synapse Analytics provide powerful analytics capabilities, allowing users to run complex queries and generate detailed reports. These tools help businesses make data-driven decisions by providing actionable insights derived from their data.
Data visualization tools transform raw data into graphical representations such as charts, graphs, and dashboards. These visual aids make it easier to understand complex data sets and communicate insights effectively to stakeholders. By presenting data in an intuitive and visually appealing way, data visualization tools help organisations identify trends, monitor performance, and make informed decisions.
“Our mentality was that while we’re young and agile, let’s implement the right infrastructure as early as possible. It was all about finding the right partnership to get this in place. What we have achieved is a really robust and stable data model that gives us the ability to provide retail insights across all of our customers”
In this 2-3 hour data platform audit workshop, we focus on understanding your existing data platforms, their design and implementation. We look at the core drivers for change such as performance, cost and design and we explore remedial actions versus your appetite for implementing new technology.
Your platform should leverage cloud-based solutions with Transparity’s data engineering solutions to ensure scalability, flexibility, and cost-effectiveness. Key components include data ingestion tools, real-time processing capabilities, and robust data storage solutions.
As a Microsoft Data Partner, Transparity’s Data Engineering Consultancy and data engineering & cloud migration services focus on the following data products.
Microsoft Fabric is a comprehensive, end-to-end data management solution that integrates data engineering, data warehousing, and data science capabilities. It enables organisations to streamline data workflows, ensuring consistency and enhancing productivity.
Databricks, the unified analytics platform, leverages Apache Spark to provide data engineers and data scientists with powerful tools for collaborative analytics. It supports large-scale data processing and machine learning, facilitating rapid insights and innovation.
Azure SQL is a cloud-based relational database service that offers high availability, scalability, and security. Designed for modern applications, it combines the best of SQL Server technology with the flexibility of the cloud.
Azure Synapse Analytics is an integrated analytics service that combines big data and data warehousing. It allows for the seamless querying of both relational and non-relational data, empowering organisations to gain deeper insights from their data.
SQL Server Modernisation involves updating and optimising SQL Server environments to ensure peak performance, security, and cost-efficiency. By modernising SQL Server infrastructure, organisations can leverage the latest features and capabilities to drive growth.
Transparity’s team of data engineering consultants are trusted by 100s of organisations to design, implement and manage Microsoft data services.
As an Azure Expert MSP with additional specialisations in Data Warehouse Migration and AI Platform, our team have a wealth of experience to support customers on their data and AI journey.
Data engineering is the practice of designing and building systems for collecting, storing, and analysing data at scale. It involves the development of data pipelines, ensuring data quality, and making data accessible for analysis and decision-making.
Data engineering consultants focus on the architecture, design, and deployment of data systems. They build scalable data pipelines, implement data warehouses and lakes, and ensure efficient, secure data handling. Their goal is to create a robust infrastructure that supports data collection, storage, and processing needs.
Data engineering is crucial because it ensures that data is reliable, organised, and ready for analysis. This allows organisations to make data-driven decisions, uncover insights, and drive innovation.
Common use cases for modern data platforms include:
Data engineering consultants implement various storage solutions tailored to an organisation’s needs. This includes data warehouses, data lakes, and other storage architectures. These solutions ensure optimal performance, reliability, and accessibility, supporting organisation’s data storage and retrieval requirements.
