Discover how data quality ensures effective, accurate, and reliable machine learning models with essential strategies for success.
The Critical Role of Data Quality in Machine Learning Read More »
Data & AI
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 & 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
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
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
Deliver Faster Data Value with DataOps The world of data analytics is rapidly accelerating. To stay competitive and agile, organisations need to continually adapt and invest strategically in their data culture, processes, and data platforms to ensure there is strategic alignment to the needs of their business, while enabling better agility, improved time-to-insight & higher quality data delivered to end-users. By leveraging DataOps practices, organisations can deliver faster data value in a cost-effective manner, enabling businesses to adapt and uncover insights with agility. DataOps is a lifecycle practice and collection of workflows, standards, and architecture patterns that drive agility and innovation to orchestrate data movement from data producers to data consumers, enabling the output of high-quality data with improved security. The Key Objectives of DataOps The primary objectives of DataOps (Data Operations) are to streamline and improve the overall management and delivery of data within an organisation. There are many benefits that can be reaped from leveraging DataOps practices which are summarised below: The building blocks of DataOps practices To reap the full benefits of DataOps practices requires strategic planning & investment into the organisation’s data culture. The following are a few building blocks and steps that can be taken to fully embrace DataOps practices: Conclusion: DataOps aims to enhance the overall effectiveness, efficiency, and value of data operations within an organisation, ultimately driving better business outcomes and data-driven decision-making. As the market of data analytics is rapidly accelerating, the adoption of DataOps practices is continuing to gain momentum. Organisations that wholeheartedly embrace DataOps practices and invest in driving and fostering a data-driven culture will be ideally positioned to deliver faster data value to identify opportunities and challenges and make faster decisions with confidence.
Deliver Faster Data Value with DataOps Read More »
Cloud-Native, DevSecOps
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. What are the 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.
The Modern Data Stack with dbt Framework Read More »
Data & AI
Top 5 Data Engineering Techniques in 2023 Data engineering plays a pivotal role in unlocking the true value of data. From collecting and organising vast amounts of information to building robust data pipelines, it is a complex and vital capability that is becoming more prevalent in today’s complex technology world. There are various intricacies in data engineering, while exploring its challenges, techniques, and the crucial role it plays in enabling data-driven decision making. In this blog post, we explore the top 5 trending data engineering techniques that are expected to make a significant impact in 2023. TL Consulting see Data engineering as an essential discipline that plays a critical role in maximising the value of key data assets. In recent years, several trends and technologies have emerged, shaping the field of data engineering, and offering new opportunities for businesses to harness the power of their data. These techniques enable better and more efficient management of data, unlocking valuable insights and helping enable innovation in a more targeted manner. Since Data engineering is a rapidly evolving domain, there is a continuous need to introduce new data engineering techniques and technologies to handle the increasing volume, variety, and velocity of data. Data Engineering Techniques DataOps One such trend is DataOps, an approach that focuses on streamlining and automating data engineering processes leveraging agile software engineering and DevOps. By implementing DataOps principles, organisations can achieve collaboration, agility, and continuous integration and delivery in their data operations. This approach enables faster data processing and analysis by automating data pipelines, version controlling data artefacts, and ensuring the reproducibility of data processes aligning to DevOps and CICD practices. DataOps improves quality, reduces time-to-insights, and enhances collaboration across data teams while promoting a culture of continuous improvement. DataMesh Another significant trend is Data Mesh, which addresses the challenges of scaling data engineering in large enterprises. DataMesh emphasises domain-oriented ownership of data and treats data as a product. By adopting DataMesh, organisations can establish cross-functional data teams, where each team is responsible for a specific domain and the associated data products. This approach promotes “self-service” data access through a data platform capability, empowering domain experts to manage and govern their data. Furthermore, as the data mesh gains adoption and evolves, with each team that shares their data as products, enabling data-driven innovation. Data Mesh enables scalability, agility, and improved data quality by distributing data engineering responsibilities across the organisation. Data Streaming Real-time data processing has also gained prominence with the advent of data streaming technologies. Data streaming allows organisations to process and analyse data as it arrives, enabling immediate insights and the ability to respond quickly to dynamic business conditions. Platforms like Apache Kafka, Apache Flink, Azure Stream Analytics and Amazon Kinesis provide scalable and fault-tolerant streaming capabilities. Data engineers leverage these technologies to build real-time data pipelines, facilitating real-time analytics, event-driven applications, and monitoring systems to further. This type of capability can lead to optimised real-time stream processing and can gain valuable insights into understanding of customer behaviours and trends. These insights can help you make timely and informed decisions to drive your business growth. Machine Learning The intersection of data engineering and machine learning engineering has become increasingly important. Machine learning engineering focuses on the deployment and operationalisation of machine learning models at scale. Data engineers collaborate with data scientists to develop scalable pipelines that automate the training, evaluation, and deployment of machine learning models. Technologies like TensorFlow Extended (TFX), Kubeflow, and MLflow are utilised to operationalise and manage machine learning workflows effectively. Real-time data streaming offers numerous benefits and empowers you to make informed business decisions. Data Catalogs Lastly, from our experience, Data Catalogs and metadata management solutions have become crucial for managing and discovering data assets. As data volumes grow, organising and governing data effectively becomes challenging. Data cataloguing enables users to search and discover relevant datasets and helps create a single source of knowledge for understanding business data. Metadata management solutions facilitate data lineage tracking, data quality monitoring, and data governance, ensuring data assets are well-managed and trusted. Data cataloguing accelerates analysis by minimising the time and effort that analysts spend finding and preparing data. These trends and technology advancements are reshaping the data engineering landscape, providing organisations with opportunities to optimise their data assets, accelerate insights, and make data-driven decisions with confidence. By embracing these developments, understanding your data assets and associated value, can lead to smarter informed business decisions. By embracing these trending techniques, organisations can transform their data engineering capabilities to enable some of the following benefits: Accelerated data-driven decision-making. Enhanced customer insights, transparency and understanding of customer behaviours. Improved agility and responsiveness to market trends. Increased operational efficiency and cost savings. Mitigated risks through robust data governance and security measures. Data engineering is vital for optimising organisational data assets since these are an important cornerstone of any business. It ensures data quality, integration, and accessibility, enabling effective data analysis and decision-making. By transforming raw data into valuable insights, data engineering empowers organisations to maximize the value of their data assets and gain a competitive edge in the digital landscape. TL Consulting specialises in data engineering techniques and solutions that drive transformative value for businesses enabling the above benefits. We leverage our expertise to design and implement robust data pipelines, optimize data storage and processing, and enable advanced analytics. Partner with us to unlock the full potential of your data and make data-driven decisions with confidence. Visit TL Consulting’s data services page to learn more about our service capabilities and send us an enquiry if you’d like to learn more about how our dedicated consultants can help you.
Top 5 Data Engineering Techniques in 2023 Read More »
Data & AI