Data lakes have become a popular option for big data management and analytics in recent years. However, their inability to determine data quality or the lineage of findings or uses makes them less than ideal. Additionally, without descriptive metadata and a mechanism to maintain it, data lakes often turn into data swamps. As a result, analysts must start from scratch for every use of data, which can be time-consuming and frustrating.
Thankfully, in today's data-driven world, centralized data is no longer necessary. Federated Learning allows organizations to consider other choices, such as data warehouses or data marts.
What is a data lake and why is it popular?
A data lake is a large, centralized repository for data that is not necessarily ready for analysis. This can include raw data, data in its original format, and data that has not been cleansed or transformed.
Data lakes are a common option for big data management and analytics because they have been marketed to offer several advantages over other approaches. First, they provide a single view of the data, which can be helpful for understanding the organization's data landscape. Additionally, data lakes can accept any type of data, which makes them versatile and adaptable. Finally, data lakes can be easily scaled to meet the needs of the organization.
Disadvantages of Data Lakes
Data lakes have many disadvantages compared to other big data management and analytics systems. Here are some of the most significant:
- Data lakes make it difficult to determine the quality of the data..
- Data is stored in its original format, which can make it difficult to understand and use.
- Data lakes are less scalable.
- It is difficult to track the lineage of findings or uses made from the data.
- Analysts must start from scratch for every use of data, which can be time-consuming and frustrating.
- Data lakes are less flexible.
- Data lakes provide less transparency and audit ability.
- Data lakes are less secure.
- Data lakes are less energy efficient than other options.
Due to the evolution of federated learning, centralized data is no longer necessary. Federated learning's benefits offer advantages for managing and analyzing big data compared to data lakes.
What is federated learning?
Federated Machine Learning is a promising alternative to centralized data management and analytics. Federated learning is a machine-learning technique where training data is distributed across multiple servers, rather than being centralized on one server. This approach allows organizations to keep data distributed across different data stores or silos. However, it also provides a way to join this data for analysis. As a result, federated machine learning can provide the benefits of centralized data without the disadvantages.
Benefits of federated learning
Federated machine learning is a distributed approach to machine learning that allows multiple machines to collaborate in order to learn from data. This approach is more advantageous for managing and analyzing big data than centralized data management and analytics. Here are some of the reasons why:
- Federated Learning improves privacy because it reduces the number of entities that have access to the data.
- Federated learning allows for faster training times, as different servers can work on different parts of the training dataset.
- Federated learning is scalable and can be adapted to meet the needs of the organization.
- Federated learning provides a way to stitch together data from different data stores or silos, which can be helpful for understanding the organization's data landscape.
As a result, federated learning is a promising alternative to centralized data management and analytics.
Federated learning categories
There are three main categories of federated learning:
Horizontal or Homogenous Federated Learning
In horizontal federated learning, all the machines in the network are similar, meaning they all use the same algorithm and data representation. This approach is useful for problems where a large number of machines are available to participate in the learning process.
Vertical or Heterogeneous Federated Learning
Vertical federated learning is used when there is a large difference in the abilities of the machines in the network. In this type of federated learning, each machine is responsible for a different subset of the data. This approach is useful for problems where not all machines have the same level of ability or knowledge.
Hybrid Federated Learning
Hybrid federated learning is a combination of horizontal and vertical federated learning. This approach is useful for problems where not all machines have the same level of ability or knowledge, but where there is a need to combine data from different machines.
Other big data management and analytics tools
Other, more viable, options for big data management and analytics include data warehouses or data marts. Both of these approaches have their own advantages and disadvantages; however, they may be more suitable compared to data lakes for your organization.
Data warehouses are a critical part of big data management and analytics. Data warehouses provide a single view of the data that is cleansed, transformed and standardized. This allows for accurate reporting and analysis. Data warehouses are built on a solid foundation of metadata. This metadata provides information about the data, such as its source, quality, and lineage.
Data marts are smaller, more focused data warehouses that provide timely information for specific groups or business functions. They are designed for specific purposes, such as marketing or sales. This allows them to be smaller and faster than a general data warehouse and have less redundancy. Data marts are built on the same foundation of metadata as data warehouses.
Both data warehouses and data marts offer the advantage of easily determining the quality of the data. Finally, analysts can easily track the lineage of findings or uses made from the data. As a result, data warehouses and data marts may be more suitable for some organizations than data lakes.
Data lakes have many disadvantages when compared to federated learning. These include the lack of quality control, the inability to determine the lineage of findings or uses, and the lack of governance. As a result, federated machine learning makes it a better option for managing and analyzing big data.
Have questions about your data lakes or want to learn more about federated learning? One of our DATA BOSSES can help you get the most out of your data!