In today’s ever-changing data environment, data architects and software engineers need to be well-versed in federated data lakes in order to unlock the full value of their organizations’ data assets. Federated data lakes offer scalability, flexibility and integration capabilities to address the challenges of big data. By understanding and utilizing Federated Data Lakes and Virtual Data Lakehouses, developers and data architects can create robust, scalable and future-proof data solutions that open data access, enhance their digitalization across the org and drive innovation.
With a Virtual Data Lakehouse, organizations and businesses can leapfrog into a much higher level of digitalization, they can adopt AI much more quickly than their competitors, gain faster market shares and reach their digital transformation goals and stay ahead in this era of data-first technologies.
For years, data has been at the core of every company in every sector. As the volume and complexity of data increase, so does the complexity of managing, analyzing, and extracting value from the growing amount of data across different data silos and data lake solutions. This is where Virtual Data Lakehouses (VDLs) come into play. In today’s blog, we will explore the reasons why organizations and companies should invest in or educate their tech employees on federated data architectures and software engineering. In addition, we will explore why you will need to understand federated data lakes and VDLs and how this understanding can help you optimize your data assets.
Understanding Federated Data Processing
Before we get into the why's and how's of federated data lakes for software engineering and data architects, let's first define what it is.
Federated data processing is a distributed data management system that allows data to be stored and analyzed across multiple data sources in a unified, scalable way. It differs from traditional data lakes in that federated data lakes do not require data to be “pre-defined” or “structured” like traditional data lakes. Instead, data can be stored in a wide range of data formats in databases like Snowflake or Oracle, data lakes like Cloudera or Databricks and data types (e.g. relational database, text files, images, etc.). In many cases, data does not need to be copied or moved into one specific data lake. Instead, the user can directly execute data tasks at the data source.
Understanding The Virtual Data Layer
With a Virtual Data Layer, you can access mostly all of your data into one big, interconnected data lake. It's also known as a Federated Data Lakehouse, and it allows you to store and analyze your data across different storage systems. With Scalytics Connect, you can do this without having to send your data to a central server or data lake like Spark or Databricks - a great way to increase data scalability, increase data processing, and multiply your data analytics capabilities without sacrificing speed, privacy, and security. Our flagship product Scalytics Connect lets companies and large organizations use data analytics, ML, or LLM models on a wide range of devices, edges, and data lakes, warehouses, or storage systems.
The Role of Software Engineers
As data-driven technologies continue to evolve, software engineers find themselves at the forefront of developing robust and scalable solutions that can effectively handle large and diverse datasets. Here's why software engineers should know federated data lakes:
1. Scalability and Flexibility: Federated data lakes provide unparalleled scalability, enabling software engineers to design solutions that can handle increasing data volumes without sacrificing performance. Additionally, the flexibility offered by federated data lakes allows software engineers to work with raw, unstructured data, enabling them to experiment with different data models and iterate quickly.
2. Easy Integration: Federated data lakes seamlessly integrate with various data sources and tools, including external APIs, databases, and cloud-based storage solutions. This integration provides software engineers with a holistic view of the data, allowing them to develop comprehensive solutions by leveraging data from diverse sources.
3. Advanced Analytics and Machine Learning: Federated data lakes support integration with advanced analytics tools and machine learning frameworks. This empowers software engineers to unlock valuable insights and build intelligent applications that can make data-driven predictions and recommendations, driving innovation within their organizations.
The Role of Data Architects
Data architects play a crucial role in designing and implementing effective data management strategies within organizations. Here's why data architects should familiarize themselves with federated data lakes:
1. Data Accessibility and Governance: Virtual Data Lakehouses provide a unified and centralized view of an organization's data assets, simplifying data accessibility and governance. Data architects can leverage federated data lakes to access and manage data from different sources without needing to physically move or replicate it, ensuring consistency and integrity across the organization.
2. Cost Efficiency and Scalability: With Virtual Data Lakehouses, data architects can avoid the costly process of data transformation and migration to a single repository. Instead, they can leverage existing data sources and extract value from them in a scalable way. This approach reduces storage costs and eliminates the need for extensive data duplication.
3. Integration with Advanced Analytics: Virtual Data Lakehouses integrate seamlessly with various advanced analytics tools, enabling data architects to derive valuable insights and make data-driven decisions. By combining multiple data sources and applying sophisticated analytics, organizations can unlock new opportunities and gain a competitive edge in their respective markets.
Software engineers and data architects: A symbiotic relationship for Virtual Data Lakehouses
The role of software engineers and data architects is one of the most critical in any data driven organization. Although they have distinct roles, the relationship between the two is critical to their success.
Software engineers create applications that work with data lakes. With their expertise in coding languages, data structures, and algorithms, software engineers create software that can extract data, transform data, and load it into a data lake or data warehouse. They work together with data scientists, analysts, and others to develop applications that help users understand the data.
Since Data Architecture is responsible for conceptualizing and executing a data lake, its primary goal is to create a virtual, holistic view of all data sources an organization may have. A data architect collaborates with stakeholders to identify business requirements and then develops a data lake concept that meets those requirements. A data architect also collaborates with software engineers to ensure that the applications that leverage the data lake architecture are compatible.
Collaboration and synergy between software engineers and data architects are essential for success in utilizing Virtual Data Lakehouses effectively. By working together, organizations can unlock new opportunities and gain a competitive edge in their respective markets.
Software engineering and the data architecture have a symbiotic relationship. A software engineer needs the data architect to give them a well-thought-out data lake to build applications on. The data architect needs the software engineer to execute their designs and build applications that help users understand the data. For the first time, virtual Data Lakehouses allow software engineers to work with data architects to build cross-functional solutions using federated data lakes that leverage the power of data lakes.
Here are a few real-world examples of how data architects and software engineers can collaborate to get the most out of Virtual Data Lakehouses:
- Software engineers develop applications that can automatically feed data from various sources into a data lake. This saves time and effort for data analysts and scientists, as well as keeping the data lake up to date.
- With the help of data architects and software engineers, data pipelines can be created to move data out of the data lake and into other systems (e.g. data warehouses or BI tools). This not only helps organizations make more efficient use of the information in their data lake, but it also helps ensure the data is safe and compliant.
- With the help of software engineering and data architects, companies and organizations can develop applications that help users understand the data in their data lake. Such applications can leverage machine learning (ML), AI and other tools to discover trends and patterns in your data, and present that information in a user-friendly way.
Software engineers and data architects should be familiar with Virtual Data Lakehouses (VDLs) to optimize their data assets. VDLs are distributed data management systems that store and analyze data across multiple data sources in a single, unified, and scalable way. Software engineers benefit from federated data lakes' scalability, flexibility, easy integration, and advanced analytics and machine learning capabilities. Data architects play a crucial role in designing and implementing effective data management strategies, ensuring data accessibility, governance, cost efficiency, and integration with advanced analytics tools.
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