Close to 89% of businesses face challenges with data integration. Nearly 40% of projects fail due to the complications in integrating fragmented data sets from different sources. Poor data quality is cited as a major reason for businesses worldwide to incur significant losses (amounting to nearly $15 million annually).
Organizations across industries grapple with merging disparate data sources, ensuring data quality and consistency, and maximizing the performance of data processing pipelines. The key to navigating this vast sea of digital information lies in streamlining the data engineering process.
Today, data engineering extends beyond database management to include advanced analytics, AI-driven transformations, and more.
Industry-wide Challenges in Data Engineering
The challenges faced in data engineering are not limited to an organization or its implementation. It spans across analytics projects irrespective of the use case. Some of the major challenges include:
Lack of Interoperability – Storage and computing are decoupled in many big data implementations, leading to authentication and policy struggles. This further impacts the creation of unified integration flows, which become overhead due to infra setup and maintenance.
Missing Ownership of Data Products – Data domains are often stored across different storage accounts and cloud platforms, resulting in massive data silos and difficulties in managing data products effectively.
Data Lineage Breakage – With multiple data pipelines operating at different frequencies, maintaining data lineage becomes a challenge, leading to potential data inconsistencies and errors.
Absence of Centralized Pipeline Maintenance – A lack of a centralized point for pipeline maintenance will affect an organization’s ability to efficiently manage and monitor data movements across different projects.
Cost Management Complexity – Inadequate insights into product, table, and query performance make it challenging to pinpoint the root cause of budget overruns in infrastructure and operations.
The FlowMaster
Microsoft Fabric, the robust platform, has been a game changer in aiding the seamless integration of data across various sources, further structuring the data engineering process.
LatentView has leveraged Fabric’s in-house capabilities and designed a single-centric framework for all your data needs. The framework provides an end-to-end solution, from sourcing to visualization/reporting of data or modern data science needs, with an exception for the semantic layer, as it varies based on business needs.
FlowMaster Architecture
Below is a quick view of how the FlowMaster is designed in Fabric.
Data is extracted from the Lakehouse source to the bronze table via Data Factory, ensuring structured storage. Using a notebook framework, data in the bronze table is refined and enhanced based on predefined logic, leading to the silver layer. The silver layer data is then modeled into a semantic representation, facilitating business-oriented analysis. Automation through pipelines and regular monitoring maintain the integrity and efficiency of the Extract, Transform, and Load (ELT) process, enabling informed decision-making from refined data in the gold layer.
Implementing this framework allows users to load their table and visualize it in a few quick steps:
- Metadata Table Entries: Information on the tables and transformations to be captured in these tables in the warehouse.
- Pipeline Configuration: Configure pipeline parameters for table names and execution details.
- ETL Execution: Automated pipelines based on parameters.
- Semantic Model Creation: One-time setup for each new table to create the models for Power BI consumption.
- Dashboard Integration: Integrate semantic model and performance metrics into a dashboard.
FlowMaster + Fabric
Benefits of Fabric
- Currently, numerous services are available in each cloud that can cater to the analytics needs of the data teams. However, each cloud has multiple options for licensing, cost (compute and storage), and dependencies, which can pose a challenge when working with a time-bound and cost-bound analytics implementation. Fabric allows seamless compatibility with various tools, allowing users to maintain a simple compute and storage model across teams. It further promotes the use of a single copy of data, leading to a streamlined data analytics architecture.
- Fabric components for a typical data engineering load in FlowMaster reduced storage and compute costs by 70% compared to implementing them in the Azure Synapse Analytics landscape.
- In a typical data processing framework, between storage and computing, many I/O-intensive operations happen back and forth (scanning time, caching). In Fabric, using FlowMaster, data resides in a single place -One Lake, and ADF and data engineering services have faster data access and manipulation guaranteed with our framework in Fabric.
- Fabric comes with built-in support for data lineage, making data management easy and aiding in identifying the existing data in their environment. This feature enables organizations to reclaim 50% of their time by avoiding the development of redundant data pipelines while ensuring a single version of data across all consumption points to meet growing business demands and SLA requirements.
How does FlowMaster enhance Fabric?
Using our metadata-driven automated framework solution, you can effortlessly adapt to changing business needs and reduce the risk of errors instead of developer-dependent flows and transformations.
Effort Reduction: When a new data source is onboarded into the systems, we estimate an effort reduction of 80%.
If an extension on the existing pipeline must be made, it can be handled with minimal development effort, which, depending on the magnitude of the change, will take around one week.
Hierarchy of Data Products: The Fabric is designed to be data mesh-friendly, considering the surging rate of data mesh implementation. According to a study, nearly 45% of organizations want to implement a data mesh by 2028. Our framework adds to this specific feature by facilitating data movement across various data products, forming a hierarchy of data products, projects, and pipelines.
Our tables maintain the hierarchy by the sequential numbering of the pipelines that process similar data.
Auditing: Data products maintained within a workspace can be tracked and audited by our internal dashboarding setup for auditing purposes.
Usability: FlowMaster is a completely reusable framework and a low-code platform.
Timeline and Set Up: The estimated initial setup time is around 4 weeks to establish this for a workspace. After the initial setup, the framework allows the teams to add metadata entries into the configuration table to load the data from the available types or sources.
Future Proof: FlowMaster future-proofs your data platform by leveraging audit and log tables, facilitating comprehensive audit trails and maintenance reports to swiftly diagnose performance issues and identify root causes across products, tenants, jobs, and queries.
FlowMaster Caters To:
- Newly onboarded clients or customers who have not previously engaged with other cloud products or services can leverage Fabric to rapidly deploy scalable and modular data solutions.
- Clients who are already on Azure Synapse Analytics and are looking for advanced features that offer an enhanced analytics experience in the form of a more user-friendly Software-as-a-Service (SaaS) solution.
- Azure customers should strongly consider Microsoft Fabric for seamless integration, enhanced security, transparent billing, and advanced analytics capabilities. These capabilities empower streamlined analytics processes and informed decision-making.
- Clients who have data from multiple cloud services but are heavily invested in Azure can optimally leverage the power of delta open sharing and shortcuts in One Lake. With the data being centralized, accessing and processing it will make data needs more manageable.
- Power BI customers transitioning to Microsoft Fabric bring seamless integration with Synapse, unify data sources, and offer enhanced security. Fabric’s consolidated environment establishes a reliable data foundation, delivering accurate insights and peace of mind, making it an ideal choice for elevating analytics processes.
In conclusion, FlowMaster offers a comprehensive solution to overcome the challenges associated with data integration in today’s business landscape. When combined with Fabric’s inbuilt features, it provides seamless integration, centralized data management within decentralized domain workspaces, and robust lineage tracking. Using FlowMaster with Fabric empowers organizations to streamline data operations, improve efficiency, and drive business success.
