Continue to use Grotabyte Archive as your data needs evolve, without the need to constantly upgrade or replace your archiving system.
Handle large volumes of data without any performance degradation
ENTERPRISE INFORMATION ARCHIVING
Scalable Metadata and Content Archive
Archive and organize massive amounts of silo files (pdf, office documents, images, audio, videos, meeting recordings, and more) on scalable, reliable, and secure object storage. Both metadata and file contents are indexed, archived, and search-ready.
Scalable metadata and content archiving are key features of Grotabyte Archive, our enterprise information archiving solution. With Grotabyte Archive, businesses can store and organize their data in a centralized location, making it easy to search for and access the information they need.
But what makes Grotabyte Archive truly unique is its scalability. As data volumes continue to grow, our solution can handle the increased load without issues. This means that businesses can continue to use Grotabyte Archive as their data needs evolve, without the need to constantly upgrade or replace their archiving system.
The benefits of scalable metadata and content archiving are numerous. By using Grotabyte Archive, businesses can:
Save time: With our powerful search and retrieval features, businesses can quickly and easily find the data they need, saving valuable time and improving efficiency.
Save money: By using a scalable solution, businesses can avoid the costs of constantly upgrading or replacing their archiving system as data volumes increase.
Improve compliance: By properly storing and organizing data, businesses can ensure that they are meeting any legal or regulatory requirements for data retention.
Protect data: With robust security features and data protection measures, businesses can ensure that their data is safe and secure.
In short, scalable metadata and content archiving is a game-changer for businesses looking to manage and access their data more effectively. With Grotabyte Archive, businesses can easily scale up their solution as their data needs grow, ensuring that they can always access the information they need when they need it.
Scalable technologies are those that are able to handle increasing workloads without significant degradation in performance. These technologies are essential for the scalable metadata and content archiving feature of Grotabyte Archive, as they allow the system to handle large volumes of data without any issues.
Archive and organize massive amounts of data into information
Several scalable technologies have been used to enable the scalable metadata and content archiving feature in Grotabyte Archive. These include:
Distributed storage systems: By distributing data across multiple storage devices, Grotabyte Archive is able to store large volumes of data without any performance degradation. This allows the system to scale up as data volumes increase.
Distributed search engine: Grotabyte uses Elasticsearch, a popular open-source search engine based on the Lucene library. It is a distributed search engine, meaning that it stores and processes data across a cluster of machines. Grotabyte Archive uses a load balancer to distribute incoming search requests across a cluster of Elasticsearch nodes. Each node in the cluster runs an instance of Elasticsearch and is responsible for storing and indexing a portion of the data. This allows the system to scale horizontally as the volume of data and search traffic increases.
Another important aspect of the architecture is the use of indices to store and organize the data. An index is a collection of documents that have similar characteristics. Indices can be created, deleted, and modified in Elasticsearch, and they can be configured with specific settings and mappings that control how the data is stored and indexed.
Distributed queue: Grotabyte Archive uses a distributed queue spread across multiple machines, rather than residing on a single machine. In a distributed system, a distributed queue can decouple different components of the system and enable them to scale independently.
One of the main benefits of using a distributed queue is that it allows producers (the components that generate work items), for example, email archivers, to pull emails from the mail server, and consumers (the components that process work items), for example, parse, prepare for indexing, extract entities such as PII and sentiment analysis, and index in Elasticsearch to scale independently. For example, if the rate of incoming work items increases, the producer can scale up by adding more machines to generate the work items. Similarly, if the rate of work processing increases, the consumer can scale up by adding more machines to process the work items.
Another benefit of distributed queues is that they can provide a buffer between components, allowing them to operate at different speeds. For example, if a producer generates work items faster than a consumer can process them, the work items can be enqueued in the distributed queue until the consumer is ready to process them. This can help to smooth out fluctuations in the rate of work and improve the overall stability and performance of the system.
Distributed queues can also be used to improve the reliability of a system by allowing work to be retried if it fails. For example, if a work item causes an error when it is being processed, it can be placed back in the queue and retried at a later time, rather than being lost permanently.
Load balancing: To ensure that the system can handle large requests without slowing down, Grotabyte Archive uses load balancing to distribute incoming requests across multiple servers. This helps ensure the system remains fast and responsive even under heavy load.
Data partitioning: Grotabyte Archive uses data partitioning to divide large datasets into smaller, more manageable chunks. Separate indices are created for each data source, for example, emails, transcriptions, files, and chats, each have separate indexes. This allows the system to scale up as data volumes increase, as it can easily add more servers to handle the additional load. This also helps in faster searches, especially when the filtering option is used to search data within specific data sources.
Database: In addition to the Elasticsearch cluster, Grotabyte Archive includes a database to store user information and other data. The database stores information such as user profiles, search history, server information, data source configuration, and other metadata.
Near-real-time Data Flow: Grotabyte uses Apache NiFi in the movement of data between disparate systems in an automated and reliable manner. NiFi includes features such as data buffering and built-in fault tolerance, which help to ensure that data is not lost or corrupted as it is being transferred between systems. NiFi can be run on a cluster of machines, allowing it to scale horizontally to handle large volumes of data. NiFi can connect to a wide variety of systems and data sources, making it easy to integrate with existing infrastructure. NiFi supports secure data transfer and user authentication, enabling the protection of sensitive data as it flows through the system.
By using these and other scalable technologies, Grotabyte Archive is able to provide a scalable metadata and content archiving solution that is able to handle large volumes of data without any performance degradation. This makes it an ideal solution for businesses looking to store, manage, and access their data more effectively.
Note: For smaller implementations, not all technologies or components might be required.