Automate data pipeline testing to enhance quality and efficiency. Explore benefits, challenges, and best practices to streamline your data processes.
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Data
Discover how Microsoft’s OneLake can streamline your data management. Learn about its features, benefits, and how it simplifies complex data landscapes.
Is Microsoft’s OneLake The Answer To Your Data Management? Read More »
Cloud-Native, Data
Unlock the potential of quantum computing with Azure Quantum. Explore its features, use cases, and transformative impact across industries.
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Cloud-Native, Data
Organisations often struggle with implementing and maintaining their AI and ML deployments. Read how MLOps in Databricks is addressing this.
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Data
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
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
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.
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Data
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.
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Data
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.
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Data
The Modern Data Stack with dbt Framework In today’s data-driven world, businesses rely on accurate and timely insights to make informed decisions and gain a competitive edge. However, the path from raw data to actionable insights can be challenging, requiring a robust data platform with automated transformation built-in to the pipeline, underpinned by data quality and security best practices. This is where dbt (data build tool) steps in, revolutionising the way data teams build scalable and reliable data pipelines to facilitate seamless deployments across multi-cloud environments. What is a Modern Data Stack The term modern data stack (MDS) refers to a set of technologies and tools that are commonly used together to enable organisations to collect, store, process, analyse, and visualise data in a modern and scalable fashion across cloud-based data platforms. The following diagram illustrates a sample set of tools & technologies that may exist within a typical modern data stack: The modern data stack has included dbt as a core part of the transformation layer. What is dbt (data build tool)? dbt (i.e. data build tool) is an open-source data transformation & modelling tool to build, test and maintain data infrastructures for organisations. The tool was built with the intention of providing a standardised approach to data transformations using simple SQL queries and is also extendible to developing models using Python. Advantages of dbt It offers several advantages for data engineers, analysts, and data teams. Key advantages include: Overall, dbt offers a powerful and flexible framework for data transformation and modeling, enabling data teams to streamline their workflows, improve code quality, and maintain scalable and reliable data pipelines in their data warehouses across multi-cloud environments. Data Quality Checkpoints Data Quality is an issue that involves a lot of components. There are lots of nuances, organisational bottlenecks, silos, and endless other reasons that make it a very challenging problem. Fortunately, dbt has a feature called dbt-checkpoint that can solve most of the issues. With dbt-checkpoint, data teams are enabled to: Data Profiling with PipeRider Data reliability just got even more reliable with better dbt integration, data assertion recommendations, and reporting enhancements. PipeRider is an open-source data reliability toolkit that connects to existing dbt-based data pipelines and provides data profiling, data quality assertions, convenient HTML reports, and integration with popular data warehouses. You can now initialise PipeRider inside your dbt project, this brings PipeRider’s profiling, assertions, and reporting features to your dbt models. PipeRider will automatically detect your dbt project settings and treat your dbt models as if they were part of your PipeRider project. This includes – How can TL Consulting help? dbt (Data Build Tool) has revolutionised data transformation and modeling with its code-driven approach, modular SQL-based models, and focus on data quality. It enables data teams to efficiently build scalable pipelines, express complex transformations, and ensure data consistency through built-in testing. By embracing dbt, organisations can unleash the full potential of their data, make informed decisions, and gain a competitive edge in the data-driven landscape. TL Consulting have strong experience implementing dbt as part of the modern data stack. We provide advisory and transformation services in the data analytics & engineering domain and can help your business design and implement production-ready data platforms across multi-cloud environments to align with your business needs and transformation goals.
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