DATA LINEAGE. THE CRYSTAL BALL YOU ALWAYS HAVE BEEN LOOKING FOR?

Data is king. Businesses rely on data to make decisions, understand their customers, and improve their operations. However, in complex organizations, it can be difficult to get insight into relevant data flows.

There are a number of reasons why this is the case. First, complex organizations often have a large number of data sources. This can make it difficult to track the movement of data between different systems and applications. Second, data flows in complex organizations can be complex and dynamic. This means that the path that data takes can change over time, making it difficult to keep track of. Third, data in complex organizations is often siloed. This means that data is stored in different systems and applications, making it difficult to get a holistic view of the data.

As a result of these challenges, it can be difficult to get insight into data flows in complex organizations. This can make it difficult to ensure the quality of data, comply with regulations, and make informed decisions. Despite these challenges, there are a number of things that organizations can do to get insight into data flows. Data lineage is typically a fundamental capability.

What is Data Lineage?

Data lineage is the tracking of data as it flows through an organization’s systems and processes. It provides a detailed record of the data’s origin, transformation, and destination. Data lineage is important for a number of reasons, including:

  • Data quality: Data lineage can help to ensure the quality of data by providing a way to track its history and identify any potential problems. For example, if a data point changes unexpectedly, data lineage can be used to trace back the change and identify the source of the problem.
  • Compliance: Data lineage can help organizations to comply with regulations that require them to track the movement of data. For example, the European Union’s General Data Protection Regulation (GDPR) requires organizations to track the personal data of their customers. Data lineage can help organizations to meet this requirement by providing a record of how personal data is collected, used, and stored.
  • Auditing: Data lineage can be used to audit data flows and identify potential security risks. For example, if an organization is concerned about the possibility of a data breach, data lineage can be used to track the movement of sensitive data and identify any potential vulnerabilities.
  • Business intelligence: Data lineage can help organizations to gain insights into their data by providing a way to track the relationships between different data sets. For example, data lineage can be used to identify which data sets are used to calculate a particular metric. This information can then be used to improve the accuracy of the metric and make better business decisions.

Benefits

  • Improved data governance: Data lineage can help organizations to improve their data governance practices by providing a way to track the ownership, access, and usage of data. This information can then be used to develop and enforce data policies and procedures.
  • Reduced risk of data loss: Data lineage can help to reduce the risk of data loss by providing a way to track the location of data. This information can then be used to recover data in the event of a loss or corruption.
  • Increased data agility: Data lineage can help organizations to become more data agile by providing a way to track the changes to data. This information can then be used to update data models and applications as needed.

How to Implement Data Lineage

There are a number of ways to implement data lineage. One way is to use a data lineage tool. These tools can help to automate the tracking of data flows and provide a visual representation of data lineage. Another way to implement data lineage is to manually track data flows. This can be done by creating spreadsheets or diagrams that track the movement of data.

The best way to implement data lineage will depend on the specific needs of the organization. However, all organizations should consider implementing data lineage to improve the quality and usability of their data.

A sample data lineage diagram illustrating the high-level data flow between a Tableau Workbook and a Snowflake datawarehouse.


Conclusion

In summary, data lineage is an important tool for organizations that want to ensure the quality, compliance, security, and usability of their data. By tracking the movement of data, data lineage can help organizations to identify and address problems, comply with regulations, and gain insights into their data.


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BUSINESS GLOSSARY VS DATA CATALOG: A TALE OF TWO CITIES?

Business glossaries and data catalogs play vital roles within data management. They are essential components in virtually any data architecture, but their purposes and interconnections are not always clear to everyone and as such worth exploring.

Exploring the relationships

A business glossary and a data catalog are closely related components within the field of data management. They both serve the purpose of organizing and documenting information about data assets within an organization, but they focus on different aspects.

Business Glossaries – Establishing the common language

A business glossary is a centralized repository or collection of terms and definitions that are specific to the organization’s business domain. It provides a common understanding and consistent definition of business terms used across different departments and stakeholders. The business glossary helps ensure clear communication and alignment between business users, data professionals, and technical teams by establishing a shared vocabulary.

Data Catalogs – Unveiling the data landscape

On the other hand, a data catalog is a comprehensive inventory of the available data assets within an organization. It provides detailed information about the structure, content, and characteristics of each dataset or data source. A data catalog captures metadata about the data, including data lineage, data sources, data quality, and other relevant information. It serves as a valuable reference for data consumers, data analysts, and data scientists to discover, understand, and effectively utilize the available data assets.

Complementary forces

The link between a business glossary and a data catalog lies in their complementary roles in facilitating data understanding and governance. While the business glossary focuses on defining business terms and ensuring consistent business vocabulary, the data catalog provides technical information about the underlying data assets. The business glossary helps users interpret and understand the data catalog by providing clear definitions of the business terms used in the metadata descriptions. In turn, the data catalog helps enrich the business glossary by associating technical metadata with the corresponding business terms, enhancing the overall understanding of the data assets and their context within the organization.

By integrating a business glossary with a data catalog, organizations can bridge the gap between business and technical perspectives, fostering better collaboration, data governance, and data-driven decision-making.


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SAP DATASPHERE: GAME-CHANGING LEAP WITH COLLIBRA, CONFLUENT, DATABRICKS & DATAROBOT PARTNERSHIPS

What is the SAP Datasphere announcement all about?

SAP has unveiled the SAP Datasphere solution, the latest iteration of its data management portfolio that simplifies customer access to business-ready data across their data landscape. In addition, SAP has formed strategic partnerships with leading data and AI companies such as Collibra, Confluent, Databricks and DataRobot to enrich the SAP Datasphere and help organizations develop a unified data architecture that securely combines SAP and non-SAP data.


What is SAP Datasphere?

SAP Datasphere is a comprehensive data service that delivers seamless and scalable access to mission-critical business data and is in essence the next generation of SAP Data Warehouse Cloud. SAP has kept all the capabilities of SAP Data Warehouse Cloud and added newly available data integration, data cataloging, and semantic modeling features, which we will continue to build on in the future. More info on the official SAP Datasphere solution page.



Why does this matter to you?

The announcement is significant because it eliminates the complexity associated with accessing and using data from disparate systems and locations, spanning cloud providers, data vendors, and on-premise systems. Customers have traditionally had to extract data from original sources and export it to a central location, losing critical business context along the way and needing dedicated IT projects and manual effort to recapture it. With SAP Datasphere, customers can create a business data fabric architecture that quickly delivers meaningful data with the business context and logic intact, thereby eliminating the hidden data tax.

As a solution partner in this ecosystem, we are excited about the collaboration and the added value it provides:

  • With Collibra SAP customers can deliver an end-to-end view of a modern data stack across both SAP and non-SAP systems, enabling them to deliver accurate and trusted data for every use, every user, and across every source.
  • Confluent and SAP are working together to make it easier than ever to connect SAP software data to external data with Confluent in real-time to power meaningful customer experiences and business operations.
  • Databricks and SAP share a vision to simplify analytics and AI with a unified data lakehouse, enabling them to share data while preserving critical business context.
  • DataRobot and SAP’s joint customers can now also leverage machine learning models trained on their business data with speed and scale to see value faster, using the SAP Datasphere as the foundation layer.

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TO CURE OR TO OBSERVE? HOW DATA OBSERVABILITY DIFFERS FROM DATA CURATION

In the world of data management, there are many terms and concepts that can be confusing. Two such concepts are data observability and data curation. While both are important for ensuring data accuracy and reliability, they have distinct differences. In this article, we will explore the key differences between data observability and data curation.

What is Data Observability?

Data observability refers to the ability to monitor and understand the behavior of data in real-time. It is the process of tracking, collecting, and analyzing data to identify any anomalies or issues. Data observability is often used in the context of monitoring data pipelines, where it can be used to identify issues such as data loss, data corruption, or unexpected changes in data patterns.

Data observability relies on metrics, logs, and other data sources to provide visibility into the behavior of data. By analyzing this data, it is possible to identify patterns and trends that can be used to optimize data pipelines and improve data quality.

What is Data Curation?

Data curation, on the other hand, refers to the process of managing and maintaining data over its entire lifecycle. It is the process of collecting, organizing, and managing data to ensure its accuracy, completeness, and reliability. Data curation involves tasks such as data cleaning, data validation, and data enrichment.

Data curation is essential for ensuring that data is accurate and reliable. It involves the use of automated tools and manual processes to ensure that data is properly labeled, formatted, and stored. Data curation is particularly important for organizations that rely heavily on data analytics, as inaccurate or incomplete data can lead to faulty insights and poor decision-making.

Key Differences Between Data Observability and Data Curation

While data observability and data curation share some similarities, there are key differences between the two concepts. The main differences are as follows:

  • Focus: Data observability focuses on monitoring data in real-time, while data curation focuses on managing data over its entire lifecycle.

  • Purpose: Data observability is used to identify and troubleshoot issues in data pipelines, while data curation is used to ensure data accuracy and reliability.

  • Approach: Data observability relies on monitoring tools and real-time analysis, while data curation relies on automated tools and manual processes.

Conclusion

In summary, data observability and data curation are two important concepts in the world of data management. While they share some similarities, they have distinct differences. Data observability is focused on real-time monitoring and troubleshooting, while data curation is focused on ensuring data accuracy and reliability over its entire lifecycle. Both concepts are important for ensuring that data is accurate, reliable, and useful for making informed decisions.

TRANSLYTICAL DATA PLATFORMS: THE FUTURE OF DATA MANAGEMENT?

As data continues to proliferate at an unprecedented rate, organizations require a powerful and flexible solution to manage, store, and analyze their data. Translytical data platforms are a new type of database management system that combines the capabilities of transactional and analytical databases. They enable businesses to perform transactional processing and analytics on the same data simultaneously in real-time or near real-time, without complex and costly ETL processes.

What are Translytical Data Platforms?

Translytical data platforms are a new class of database management systems that combine the capabilities of transactional and analytical databases. They provide the ability to process transactions and analytics simultaneously in real-time or near real-time, without the need for complex and costly ETL (Extract, Transform, Load) processes.

In other words, translytical data platforms enable businesses to perform transactional processing and analytics on the same data at the same time, resulting in faster insights and improved decision-making. These platforms are designed to handle the complexity of modern data, including structured, semi-structured, and unstructured data.

How are Translytical Data Platforms Different from Traditional Databases?

Traditional databases are designed for either transactional processing or analytics. Transactional databases are optimized for storing and processing large volumes of data related to business transactions, such as sales, inventory, and customer interactions. They ensure data consistency, accuracy, and reliability, but are not suitable for complex queries and analytics.

On the other hand, analytical databases are optimized for complex queries and reporting. They provide fast access to historical data for analysis and decision-making. However, they are not optimized for transactional processing and may require ETL processes to combine data from multiple sources.

Translytical data platforms bridge the gap between transactional and analytical databases by providing a single platform for processing transactions and analytics simultaneously. They enable businesses to perform real-time analytics on transactional data, eliminate the need for separate transactional and analytical databases, and reduce data duplication and latency.

Benefits of Translytical Data Platforms

      1. Real-Time Analytics: Translytical data platforms enable businesses to perform real-time analytics on transactional data. This means that they can get faster insights, make decisions quickly, and respond to changing business conditions.

      2. Flexible AI Foundation: Overall, translytical data platforms can provide a powerful foundation for AI applications, enabling organizations to process large amounts of data quickly and efficiently, and to gain real-time insights that can improve the accuracy and effectiveness of AI models.

      3. Simplified Data Architecture: By eliminating the need for separate transactional and analytical databases, translytical data platforms simplify data architecture and reduce data duplication and latency

      4. Improved Data Quality: Translytical data platforms ensure data consistency, accuracy, and reliability by processing transactions and analytics on the same data.

      5. Cost Savings: Translytical data platforms eliminate the need for complex ETL processes and multiple databases, reducing the cost of infrastructure and maintenance.

Conclusion

Translytical data platforms are the future of data management in general. They provide businesses with the ability to process transactions and analytics simultaneously, in real-time or near real-time, without the need for complex and costly ETL processes. With the ability to handle structured, semi-structured, and unstructured data, translytical data platforms provide faster insights, simplified data architecture, improved data quality, and cost savings. As the volume and complexity of data continue to grow, translytical data platforms will become essential for businesses to stay competitive and make informed decisions.




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DATA FABRIC VS DATA MESH: AN APPLES & ORANGES STORY?

Data fabric and data mesh are two concepts that have gained a lot of attention in the world of data management. While they share some similarities, they have some fundamental differences that are important to understand. In this article, we will explain the difference between data fabric vs data mesh.

What is a Data Fabric?

A data fabric is an architecture that provides a unified and consistent view of an organization’s data, regardless of where it resides, how it’s stored, or how it’s accessed. It allows data to flow seamlessly between different systems and applications, while maintaining data integrity, security, and governance. It provides a way to seamlessly integrate and access data from different systems, applications, databases, and clouds, making it easier for organizations to derive insights and make decisions.

What is a Data Mesh?

A data mesh is a decentralized approach to data management that empowers teams to own and manage their own data domains. It recognizes that data is a product and treats it as such, with individual teams responsible for their own data products. The goal of a data mesh is to enable faster and more efficient data delivery by allowing teams to work independently and iterate quickly.

Difference between Data Fabric and Data Mesh

The fundamental difference between data fabric and data mesh is in their approach to data management. A data fabric is a centralized approach, while a data mesh is a decentralized approach. A data fabric provides a unified and consistent view of an organization’s data, while a data mesh enables teams to own and manage their own data domains.

Another key difference between data fabric vs data mesh is in their focus. A data fabric focuses on providing a seamless and consistent view of data across an organization, while a data mesh focuses on empowering teams to own and manage their own data domains. A data mesh typically creates more room for innovation.

Conclusion

In conclusion, while data fabric and data mesh share some similarities, they have fundamental differences in their approach to data management. A data fabric is a centralized approach that provides a unified and consistent view of an organization’s data, while a data mesh is a decentralized approach that empowers teams to own and manage their own data domains. Both approaches have their own advantages and disadvantages, and the choice between the two will depend on the specific needs of the organization. 

It is worth noting that choosing between data mesh and fabric is not always a binary decision. Some parts of an organization may choose to implement data mesh, while others may prefer the data fabric approach.


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UNDERSTANDING DATA LAKEHOUSES: THE HIGHWAY TO MODERN BIG DATA MANAGEMENT OR A U-TURN TO YOUR DATA SWAMP?



As the volume and complexity of data continue to grow, traditional data management solutions are struggling to keep up. This is where data lakehouses come in. In this article, we’ll take a closer look at what data lakehouses are, how they differ from traditional data warehouses, and their benefits for businesses.

What is a Data Lakehouse?

A data lakehouse is a modern data management architecture that combines the best features of data lakes and data warehouses. It provides a unified platform for storing, managing, and processing structured and unstructured data in real-time or near real-time. Unlike traditional data warehouses, which require data to be pre-processed before storage, data lakehouses allow businesses to store raw data in its native format.

In other words, data lakehouses are designed to handle the complexity of modern data, including structured, semi-structured, and unstructured data. They provide a single platform for storing and processing data, eliminating the need for complex ETL (Extract, Transform, Load) processes.

How are Data Lakehouses Different from Traditional Data Warehouses?

Traditional data warehouses are designed to store structured data in a pre-defined schema. Data is pre-processed and organized into tables, which can be queried using SQL (Structured Query Language). Data warehouses are optimized for reporting and analysis, but require significant data modeling and schema design effort, which can slow down the data ingestion process.

On the other hand, data lakehouses store data in its raw form, without the need for pre-processing or modeling of the data. Data is organized into data lakes, which can be queried using SQL or other query languages. Data lakehouses are optimized for data processing, enabling businesses to ingest, store, and process data in real-time or near real-time, without the need for complex ETL processes.

Benefits of Data Lakehouses

    1. Scalability: Data lakehouses are designed to scale to handle large volumes of data. They can store structured, semi-structured, and unstructured data, providing businesses with the ability to ingest and process data at scale.

    2. Flexibility: Data lakehouses allow businesses to store data in its raw form, without the need for pre-processing. This provides the flexibility to query and analyze data in real-time or near real-time.

    3. Cost Savings: Data lakehouses eliminate the need for complex ETL processes, reducing the cost of data ingestion and storage. They also provide a unified platform for storing and processing data, eliminating the need for multiple data management systems.

    4. Improved Data Quality: Data lakehouses enable businesses to store data in its native format, reducing the risk of data loss or incorrect transformed data during the ETL process.

Conclusion

Data lakehouses are the future of big data management. They provide businesses with a unified platform for storing, managing, and processing structured and unstructured data in real-time or near real-time. Unlike traditional data warehouses, data lakehouses allow businesses to store raw data in its native format, eliminating the need for complex ETL processes. With scalability, flexibility, cost savings, and improved data quality, data lakehouses are essential for businesses to stay competitive and make informed decisions in the era of big data.


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DATA FABRIC IN A NUTSCHELL

In today’s digital age, data has become one of the most valuable assets for organizations of all sizes and across all industries. However, with data being generated, stored, and accessed from multiple sources and locations, managing and analyzing it has become increasingly complex.

To address this challenge, a concept called a data fabric has emerged in the field of data management. A data fabric is an architecture that provides a unified and consistent view of an organization’s data, regardless of where it resides, how it’s stored, or how it’s accessed.

The goal of a data fabric is to seamlessly integrate and access data from different systems, applications, databases, and clouds, making it easier for organizations to derive insights and make decisions. By allowing data to flow seamlessly between different systems and applications while maintaining data integrity, security, and governance, a data fabric enables users to access and analyze data in real-time, regardless of its location, format, or type.

A typical data fabric consists of multiple layers, including:

  • data discovery
  • data integration
  • data processing
  • data delivery

 

These layers work together to provide a complete view of an organization’s data, from its origin to its destination. In addition, a data fabric can provide advanced capabilities such as data lineage, data quality, and data governance, ensuring that data is accurate, consistent, and secure throughout its lifecycle.Overall, a data fabric is designed to simplify the complexities of managing and analyzing data in today’s modern data landscape, enabling organizations to be more agile, responsive, and competitive. By implementing a data fabric, organizations can eliminate the need for manual data integration, reduce the risk of errors, and derive insights from data more quickly and effectively. This allows organizations to make better decisions, optimize their operations.


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