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Data mesh and Data fabric are both modern data management concepts which address different challenges. Data mesh is mainly demanding decentralised domain ownership and meanwhile Data Fabric is focused on centralised data governance concept. Both have different benefits which you can experience but make sure you should have new mindset to adopt with these new approaches from traditional data management concepts. #dataengineering #datawarehouse #datamart #datalake #datamesh #datafabric #dataocean

In today's fast-paced data-driven world, understanding the nuances of data management is crucial. That's why I've written an article that dives deep into the world of Data Mesh, an innovative approach that transcends traditional technological boundaries. 🔍 This article isn't just about the tech; it's a journey through the transformative aspects of ownership and communication within the realms of data. It's tailored for data professionals, including data engineers, data architects, and business leaders who play a pivotal role in data strategy and management. 📊 Featuring insightful statistics and real-world applications, the article sheds light on how Data Mesh is reshaping the data landscape by emphasizing decentralized data architectures. 🤝 Logicbric's role in facilitating this transformation is also highlighted, demonstrating how their expertise in data engineering can empower your organization's data strategy. As we navigate this era of information, understanding and implementing effective data management strategies like Data Mesh is more important than ever. 💬 I'd love to hear your thoughts on this. How do you see Data Mesh influencing your data strategies? #dataengineer #dataanalytics #datamesh #dataanalyst #datamanagement

As enterprises increasingly seek to optimize data accessibility and operational efficiency, data mesh architecture has emerged as a potential transformative solution. This recent The Wall Street Journal article highlights key insights into this approach. Here are three essential takeaways: 1.     Enhanced Access and Modularity Data mesh architecture addresses the growing demand for enterprise data access by breaking down monolithic structures into modular domains. This involves decomposing data pipelines and embedding specialized data talent throughout the organization. 2.     Critical Implementation Steps Implementing a data mesh involves several critical steps - decomposing and distributing data, treating data as a product, providing data infrastructure as a platform, federating governance, and establishing cross-functional teams. 3.     Readiness Evaluation Organizations must evaluate their readiness before adopting data mesh architecture. This includes assessing business value alignment, understanding data and technology constraints, ensuring maturity in data management and governance, and aligning talent and culture. Mavent Analytics believes that implementing a data mesh could be a valid approach to significantly enhance data accessibility, quality, and agility for organizations. By breaking down data silos, treating data as a product, and fostering a culture of data-driven decision-making, companies can unlock the full potential of their data assets. For senior data professionals, we recommend conducting a comprehensive readiness assessment. This involves: ·      Aligning business objectives with data mesh benefits ·      Addressing technological constraints ·      Ensuring robust data management practices ·      Cultivating an adaptable organizational culture By following these guidelines, senior data professionals can effectively guide their organizations toward leveraging the full potential of data mesh architecture. Is your organization ready to transform its data strategy? Visit www.maventnanalytics.com to learn how our expertise can assist you in implementing effective data mesh architecture and enhancing your data operations. #DataMesh #DataArchitecture #DataAnalytics #MaventAnalytics #DataManagement #BusinessTransformation

𝐃𝐚𝐭𝐚 𝐋𝐢𝐧𝐞𝐚𝐠𝐞: 𝐀𝐫𝐞 𝐎𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧𝐬 𝐋𝐨𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐌𝐚𝐳𝐞? Did you ever try to untangle a mesh of strings to find that one thread you need? That’s exactly how data lineage feels for many organisations! It’s like navigating a labyrinth where every path loops back to another unexpected turn. In this intricate, vast data landscape, tracing where your data comes from and how it has evolved over time is critical for depicting the data lifecycle to ensure data quality and maintain regulatory compliance. However, this journey has its own challenges that limit orgs from accomplishing their data lineage outcomes effectively. Let us explore why data lineage seems to be a major challenge for several orgs. 🔸 𝐃𝐚𝐭𝐚 𝐬𝐢𝐥𝐨𝐞𝐬 — 𝐝𝐚𝐭𝐚 𝐝𝐢𝐬𝐩𝐞𝐫𝐬𝐞𝐝 𝐚𝐜𝐫𝐨𝐬𝐬 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 Implementing data lineage in complex environments is tough, especially in large organizations with siloed customer, sales, & marketing data. With data spread across databases, cloud platforms, and applications, visibility is often lost. Even top data management tools struggle to fully integrate lineage across this fragmented ecosystem. 🔸 𝐒𝐞𝐯𝐞𝐫𝐞 𝐠𝐫𝐚𝐧𝐮𝐥𝐚𝐫𝐢𝐭𝐲 𝐢𝐬𝐬𝐮𝐞𝐬 — 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐠𝐫𝐚𝐧𝐮𝐥𝐚𝐫𝐢𝐭𝐲 𝐥𝐞𝐯𝐞𝐥 The level of granularity is crucial to data lineage, yet many teams struggle to find the right balance. Too little granularity causes gaps in lineage, as you might lose visibility over how data was transformed or used at critical points. Too much granularity creates complexity, tracking every minor detail and cluttering the lineage view. 🔸 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐢𝐧𝐠 𝐠𝐫𝐚𝐧𝐮𝐥𝐚𝐫𝐢𝐭𝐲 𝐚𝐬 𝐚 𝐦𝐞𝐭𝐡𝐨𝐝 𝐨𝐫 𝐭𝐨𝐨𝐥 Carlos Jaime mentioned this minute observation in one of his articles, saying that data lineage is not a tech tool but a philosophy to drive transparency, accountability & intelligent use of information. Data lineage is a continuous journey of improvement and learning. It should guide orgs in transforming data into a valuable asset. Treating it as just a tool limits the ability to fully leverage data for insight and knowledge. 🔸 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐢𝐦𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐞𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐬 As Brian Perkins mentions unrealistic expectations often arise around the complexity of building data lineage. Many companies have developed data pipelines over time using diverse platforms & technologies, often without a cohesive data movement strategy. This complexity makes lineage more challenging than simply capturing metadata in a catalog—it demands dedicated resources and time to fully implement. So, how is your organisation handling these data lineage challenges? Let’s dive into the conversation and share your thoughts! 𝐈𝐦𝐚𝐠𝐞 𝐜𝐨𝐮𝐫𝐭𝐞𝐬𝐲: Qlik, Niraj Kumar, & Dr. Irina Steenbeek 𝐖𝐚𝐧𝐭 𝐦𝐨𝐫𝐞 𝐚𝐰𝐞𝐬𝐨𝐦𝐞 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐥𝐢𝐤𝐞 𝐭𝐡𝐢𝐬? 𝐒𝐢𝐠𝐧 𝐮𝐩 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dDT7FB3y

🌐 New Blog Alert: "Why Use Dremio to Implement a Data Mesh?"Our latest blog explores how Dremio, a data lakehouse platform, seamlessly aligns with the data mesh architecture to revolutionize data handling and processing. 🚀  Key Highlights from the Blog: - The Data Mesh Paradigm: Unpack the essence of a data mesh – an innovative approach that decentralizes data management and fosters domain-oriented data ownership. - Dremio's Role: Discover how Dremio's architecture and features perfectly complement the principles of data mesh, offering a unified, self-service data platform. - Transformative Benefits: Learn about the agility, scalability, improved data quality, and collaborative innovation that a data mesh, powered by Dremio, brings to organizations. 🛠  Implementing Data Mesh with Dremio: - Technical Integration: Understand the steps to integrate Dremio in your data ecosystem, from setting up user accounts to defining a semantic layer. - Empowering Domain Experts: See how Dremio empowers domain teams with self-serve data infrastructure, allowing for autonomous and efficient data management. - Robust Data Governance: Grasp the importance of decentralized governance in a data mesh and how Dremio’s advanced security features safeguard data across domains. 💡 Implementation Best Practices: - Get insights on strategic implementation, from starting small to emphasizing domain expertise and prioritizing strong data governance. 🌟 Why Dremio? - It’s not just about managing data; it's about transforming how your organization interacts with data. With Dremio, you can harness the full power of data mesh to enhance agility, foster collaboration, and make data-driven decisions with unprecedented efficiency. 🔗 Dive into our blog for a comprehensive look at leveraging Dremio in your journey towards a decentralized, efficient, and innovative data ecosystem. Transform your data strategy today! ://https://lnkd.in/ggb2F3ke #Dremio #DataMesh #DataManagement #DataLakehouse #BigData #DataAnalytics #DataGovernance #TechInnovation #DigitalTransformation

Data Innovation Summit 2024: done! We have been very busy presenting #witboost. Let's see how all this went: 🤏 Getting context from our guests - What's the business - How many data practitioners - How many data sets - How data are organized - How data are governed - 🚨 Major data issues mentioned by our guests - Data Catalogues not sufficient for Discoverability - Data Quality is missing mostly everywhere - where Data Quality is available, data consumers still do not have visibility - Data Sources have unstable schemas, always breaking downstream pipelines - Data Governance is still traditional, no automation - Data Stewards keep investing mandays fixing metadata as a post production activity - GenAI is appealing, no one knows where to start from - Data Quality is a problem for genAI - Data projects are expensive and no clue on what's really delivered 🤨 Still unclear concepts in the data world: - Data Marketplace vs Data Catalogue - Data Contracts in Data Governance - Self Service Platform vs Self Service BI vs Data Democratization 💁 We are here to help, clarify and guide Feel free to reach me out any moment. How can we #witboost our guests? 🏗️ Templates: - ♻️Build once and use forever - ⚙️Standardization and interoperability - 🧩Composability - 👨💻 Governance Shift Left - 📃Declarative approach - 📐Code architecture - 👨🍳Code processes - ⚖️Code policies - 🦾Code practices - 🕵️ Compliance monitoring - ✅ Control over processes, architecture, infrastructure, change management, data contracts, ... 🤝 Marketplace - 📊Trusted data - 👨🍳Trusted process - ⚖️Trusted governance Some take aways from us: - 🥡Good data for consumers means reliable data producers - 🥡Policy enforcement for data producers must be balanced by great self service capabilities and low cognitive load to build use cases - 🥡No governance on processes, no governance at all - 🥡No automation, no real governance - 🥡Governance on architecture is more powerful than governance on infrastructure only - 🥡Align data, software, infrastructure, metadata lifecycle. Fixing metadata as a post production activity just doesn't work - 🥡Lean data production requires end to end process governance 🧗Build, govern, discover! 🙏I would like to thanks all the guests approaching our booth giving us feedback, discussing concerns, exchanging ideas. 🙏Beautiful experience 🦸 🙏Thankful to my data network, nice to meet brilliant professionals in person. Keep going together to improve, build new practices, innovate. 🙏Grateful to DIS for this opportunity. 🙏 Lovable Witboost Team! #analytics #data #datamanagement #datagovernance #dataengineering #dataplatform #genai #finops #datastrategy #lean #costmanagement #costreduction #dataquality #cdo #cio #cto

In the past, data platforms were simply stepping stones, a side project handled by your data team. Not any longer, as modern firms begin to see the true value of these platforms, treating them as production-grade software, or data-as-a-service. The resources dedicated to nurturing these platforms are no longer considered ancillary but absolutely crucial. A data platform is a consolidated repository and processing hub for all data within an organisation. It's responsible for gathering, cleaning, transforming, and utilizing data to create valuable business insights. Companies that give priority to their data have found data platforms to be an effective method for collecting, operationalizing, and democratizing data on a large scale. The advent of data mesh architecture and the rise of data products have necessitated viewing the data platform as an integral engine for their development, management, exposure, and regulation. #DataPlatforms #DataAsAService #DataDriven #DataMesh #DataProducts #DataInsights #DataTransformation #DataManagement #DataDemocratization #DataArchitecture #BusinessIntelligence #DataStrategy #TechInnovation #DigitalTransformation #DataEngineering #Nerp #Nirmalyalabs https://lnkd.in/g3Nq9AYN

“Traditional data management with its massive data warehouses is facing a formidable challenger — data mesh,” says Krzysztof Wiśniewski, Senior System Architect at Striped Giraffe. “While data warehouses centralize control over the data, data mesh enables the various business units to handle the data in an agile and efficient way.” Data mesh, an innovative approach to managing data, is all about decentralization. It is very different from the monolithic data warehouses that many companies currently rely on. This architectural change can be seen as both innovative and controversial. “Take the healthcare sector,” explains Wiśniewski, “a data mesh could allow different departments — from patient care to research — to manage their data autonomously, which would improve both the quality of care and the pace of medical research.” “However, the decentralization inherent in the data mesh sparks a debate about data quality,” Wiśniewski emphasizes. “When ownership of the data is spread across different teams, there is a risk that standards and practices will be inconsistent. But this is the beauty of data mesh — it forces us to innovate in data governance.” It ensures that while each team has extensive autonomy, there is a cohesive framework that determines data quality standards. It is a delicate balance between independence and consistency, and that is what makes data mesh so groundbreaking. The potential of data mesh goes beyond simply breaking down data silos. “It is a shift in thinking,” says Wiśniewski. “Teams need to start thinking of their data as a product for the entire company — with a focus on quality, accessibility, and cross-departmental value.” But Wiśniewski warns: “This is not a one-size-fits-all solution. Every company needs to weigh up its structure, culture, and goals. The key is to align the principles of data mesh with the unique requirements of the business.” In conclusion, Wiśniewski emphasizes: “Data mesh is not only a technological change, but also a strategic one. It challenges the status quo of data management and promises a more democratic and efficient future, but also requires a careful, tailored approach.” #datamesh #dataarchitecture #datamanagement #datagovernance #dataquality #datademocratization

#The Debate: Data Lakes vs. Data Warehouses In the realm of data engineering, a significant debate persists: Data Lakes or Data Warehouses? Both have their proponents, and each offers unique advantages and challenges. As organizations strive to harness the power of their data, choosing the right storage solution is more critical than ever. #Data Lakes: The Flexibility Advantage 1. Schema-on-Read: Data lakes allow for flexible data ingestion without predefined schemas, making them ideal for unstructured or semi-structured data. 2. Cost-Effective Storage: They leverage cheaper storage solutions, which is beneficial for large volumes of raw data. 3. Advanced Analytics: Data lakes support advanced analytics and machine learning applications, as they can store vast amounts of varied data. #Data Warehouses: The Performance Powerhouse 1. Schema-on-Write: Data warehouses enforce structured data storage, ensuring data quality and consistency. 2. Optimized Query Performance: Designed for complex queries and reporting, data warehouses provide faster and more efficient data retrieval. 3. Business Intelligence: They are the backbone of traditional business intelligence (BI) systems, offering robust tools for reporting and data visualization. #The Controversy: Choosing the Right Path 1. Flexibility vs. Structure: While data lakes offer unparalleled flexibility, they can become data swamps without proper governance. Data warehouses, although more rigid, ensure data integrity and performance. 2. Cost vs. Performance: Data lakes are generally more cost-effective but can incur hidden costs related to data processing and retrieval. Data warehouses, while more expensive upfront, deliver high performance and reliability. 3. Integration and Governance: Effective data governance is essential for both. However, integrating a data lake with a data warehouse (the so-called "lakehouse" approach) is gaining traction, promising the best of both worlds. What's the Best Choice for Your Organization? There is no one-size-fits-all answer. The decision should be based on specific business needs, existing infrastructure, and long-term data strategy. Some organizations might benefit from a hybrid approach, combining the flexibility of data lakes with the performance of data warehouses. This debate will continue as technology evolves and new solutions emerge. What’s your stance? Do you lean towards the flexibility of data lakes or the structured efficiency of data warehouses? Share your thoughts and experiences in the comments below! #DataEngineering #DataLakes #DataWarehouses #BigData #TechDebate

𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐢𝐧 𝐌𝐨𝐝𝐞𝐫𝐧 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 In today's digital landscape, customer data is the lifeblood of business success. But as data volumes explode and sources multiply, traditional centralised data architectures are struggling to keep up. Enter the data mesh – an approach that's revolutionising how we handle customer data. Data mesh, a concept introduced by Zhamak Dehghani, decentralises data ownership and architecture. Instead of funneling all data into a central lake or warehouse, data mesh treats data as a product, managed by domain-specific teams. This paradigm shift has profound implications for customer data management: 🌳 𝑨𝒈𝒊𝒍𝒊𝒕𝒚: Domain teams can move faster, adapting to customer needs without waiting for centralized data teams. 🌳 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚: When teams own their data, they're invested in its accuracy and relevance. 🌳 𝑺𝒄𝒂𝒍𝒂𝒃𝒊𝒍𝒊𝒕𝒚: Distributed architecture allows for better handling of growing data volumes and varieties. 🌳 𝑪𝒐𝒎𝒑𝒍𝒊𝒂𝒏𝒄𝒆: Domain-specific ownership can enhance data governance and regulatory compliance. 🌳 𝑰𝒏𝒏𝒐𝒗𝒂𝒕𝒊𝒐𝒏: Cross-functional teams can experiment more freely, driving customer-centric innovations. For customer data, this means marketing can own campaign data, sales can manage pipeline data, and support can control ticket data – all while adhering to company-wide standards for interoperability. Implementing a data mesh isn't without challenges. It requires a cultural shift, new governance models, and robust data infrastructure. But the payoff is significant: a more responsive, scalable, and innovative approach to customer data management. As we navigate an increasingly data-driven future, data mesh offers a promising path forward. It's not just about managing data; it's about unleashing its full potential to drive customer satisfaction and business growth. Are you considering a data mesh approach for your customer data? Credit: PARAMITA (GUHA) GHOSH and DATAVERSITY https://lnkd.in/g-Ct3PKV. #DataMesh #CustomerData #DataStrategy #DigitalTransformation