Unlock the potential of quantum computing with Azure Quantum. Explore its features, use cases, and transformative impact across industries.
How Azure Quantum is Pioneering the Future of Computing Read More »
Cloud-Native, Data & AI
Organisations often struggle with implementing and maintaining their AI and ML deployments. Read how MLOps in Databricks is addressing this.
MLOps in Databricks – The Key to Effective AI and Machine Learning Deployment Read More »
Data & AI
Learn how Azure Databricks can provide a robust and scalable way to secure your data beyond it advanced data analytics capabilities.
The Secret To Next Level Data Security: Azure Databricks Read More »
Cloud-Native, Data & AI
Discover the future of software development with GitHub’s Copilot, an AI-driven tool revolutionising code generation and collaboration.
GitHub Copilot: Revolutionising Coding with Generative AI Read More »
DevSecOps
Explore how big data and cloud computing revolutionise analytics, offering scalability and agility to drive growth and innovation across industries.
Unleashing the Power of Big Data in the Cloud Read More »
Cloud-Native, Data & AI
Microservices architecture has become the go-to choice for modern companies, offering unparalleled flexibility and scalability, especially in cloud-native environments. However, this architectural shift introduces new and unique security challenges, with authorisation standing out as a paramount concern.
Building a Secure & Scalable Microservices Authorisation Architecture with Kubernetes Read More »
Cloud-Native
For organisations building Data Lakehouse platforms, an important consideration is defining a structured approach to designing data ingestion patterns, encompassing best practices for each data workload that is ingested into the Data Lakehouse environment. This is crucial for organisations looking to scale with big data analytics and enable more data consumers to perform efficient decision-making, with access to enriched data in real-time. In this article, we explore some of the best practices, key considerations and common pitfalls to avoid when defining the data ingestion patterns into the Data Lakehouse platform. The Data Lakehouse Paradigm The Data Lakehouse is a modern architecture that merges the expansive storage of a Data Lake with the structured data management of a Data Warehouse. The Data Lakehouse is the latest paradigm in Data Platform Architecture, combining the capabilities and benefits of the Data Warehouse and Data Lake into a flexible, comprehensive, and unified platform to serve many use cases including: Defining the data ingestion design patterns for the Data Lakehouse requires defining a structured approach to collect and manage data workloads in the lakehouse while ensuring there are robust data quality and security controls in place as part of the data ingestion. Key Considerations for Data Ingestion Patterns: Common Pitfalls to Avoid Conclusion In summary, the Data Lakehouse is a pathway to unlocking the full potential of your data, fostering innovation, and driving business growth. With the right components and strategic approach, your organisation can leverage Data Lakehouses to stay ahead of the curve, while maintaining a unified, cost-effective data platform deployed on your Cloud environment. Designing correct data ingestion patterns will enable the Data Lakehouse platform to run efficient and scalable data pipelines to serve big data analytics use cases. TL Consulting are a solutions partner with Microsoft in the Data & AI domain. We offer specialised and cost-effective data analytics & engineering services tailored to our customer’s needs to extract maximum business value. Our certified cloud platform & data engineering team are tool-agnostic and have high proficiency working with traditional and cloud-based data platforms. Refer to our service capabilities to find out more.
Key Considerations for Data Ingestion into the Data Lakehouse Read More »
Data & AI
In the ever-evolving landscape of data management, traditional centralised approaches often fall short of addressing the challenges posed by the increasing scale and complexity of modern data ecosystems. Enter Data Mesh, a paradigm shifts in data architecture that reimagines data as a product and decentralises data ownership and architecture. In this technical blog, we aim to start decoding Data Mesh, exploring its key concepts, principles, and market insights. What is Data Mesh? At its core, the Data Mesh is a sociotechnical approach to building a decentralised data architecture. Think of it as a web of interconnected data products owned and served by individual business domains. Each domain team owns its data, from ingestion and transformation to consumption and analysis. This ownership empowers them to manage their data with agility and cater to their specific needs. Key Principles of Data Mesh: The following diagram illustrates an example modern data ecosystem hosted on Microsoft Azure that various business domains can operationalise, govern and own independently to serve their own data analytics use cases. Challenges and Opportunities: Despite these challenges, the opportunities outweigh the hurdles. The Data Mesh offers unparalleled benefits, including: Benefits of Adopting a Data Mesh: Future Trends and Considerations: The Data Mesh is more than just a trendy architectural concept; it’s rapidly evolving into a mainstream approach for managing data in the digital enterprise. To truly understand its significance, let’s delve into some key market insights: Growing Market Value: Conclusion: In conclusion, Data Mesh represents a paradigm shift in how organisations approach data architecture and management. By treating data as a product and decentralising ownership, Data Mesh addresses the challenges of scale, complexity, and agility in modern data ecosystems. Implementing Data Mesh requires a strategic approach, embracing cultural change, and leveraging the right set of technologies to enable decentralised, domain-oriented data management. As organisations continue to grapple with the complexities of managing vast amounts of data, Data Mesh emerges as a promising framework to navigate this new frontier.
Decoding Data Mesh: A Technical Exploration Read More »
Data & AI
Accelerate State of DevOps Report (2023) by DORA In the recently released Accelerate State of DevOps Report 2023 by the DevOps Research and Assessment (DORA) team, the spotlight is on the pivotal role of user-centricity in attaining organisational excellence. This year’s comprehensive survey, spanning more than 36,000 professionals from over 1,000 organisations, uncovers a direct correlation between prioritising a user-centric approach and achieving heightened performance. Teams that prioritise understanding and aligning with user needs showcase an impressive 40% boost in organisational performance. This enhanced performance results from various factors, including: Beyond user-centricity, the report underscores several other pivotal practices that set high-performing teams apart: The DORA research team provides actionable recommendations for organisations aiming to elevate their performance: By incorporating these recommendations, organisations can empower their teams to deliver high-quality software swiftly and reliably, ultimately achieving superior organisational performance. The link to download the report can be found here: https://cloud.google.com/devops/state-of-devops
Prioritising User-Centricity to Attain Organisational Excellence Read More »
DevSecOps
As organisations continue to collect more diverse data, it is important to consider a strategic & viable approach to unify and streamline big data analytics workloads, ensuring it is optimised to drive data-driven decisions and enable teams to continue innovating and create a competitive edge. Traditionally, data warehousing has supported the need for ingesting and storing structured data, and the data lake as a separate platform for storing semi-structured/unstructured data. The data lakehouse combines the benefits and capabilities between both and bridges the gap by breaking silos created by the traditional/modern data warehouse, enabling a flexible and modern data platform to serve big data analytics, machine learning & AI workloads in a uniform manner. What is a Data Lakehouse? A data lakehouse is a modern architecture that merges the expansive storage of a data lake with the structured data management of a data warehouse. Data lakehouse platforms offer a comprehensive & flexible solution for big data analytics including Data Engineering and real-time streaming, Data Science, and Machine Learning along with Data Analytics and AI. Key Benefits of Implementing a Data Lakehouse: There are many benefits that can be derived from implementing a data lakehouse correctly: Azure Data Lakehouse Architecture: The following are some of the key services/components that constitute a typical Data Lakehouse platform hosted on Microsoft Azure: Key Considerations when transitioning to a Data Lakehouse: The following are key considerations that need to be factored in when transitioning or migrating from traditional data warehouses/data lakes to the Data Lakehouse: Implementing a Data Lakehouse: Quick Wins for Success The following are small, actionable steps that organisations can take when considering to implement a Data Lakehouse platform: Conclusion In summary, the data lakehouse is a pathway to unlocking the full potential of your data, fostering innovation, and driving business growth. With the right components and strategic approach, your organisation can leverage Data Lakehouses to stay ahead of the curve, while maintaining a unified, cost-effective data platform deployed on your Cloud environment. TL Consulting are a solutions partner with Microsoft in the Data & AI domain. We offer specialised and cost-effective data analytics & engineering services tailored to our customer’s needs to extract maximum business value. Our certified cloud platform & data engineering team are tool-agnostic and have high proficiency working with traditional and cloud-based data platforms and open-source tools. Refer to our service capabilities to find out more.
Harnessing the Power of the Data Lakehouse Read More »
Cloud-Native, Data & AI