Fully managed in the cloudStarburst GalaxySelf-managed anywhereStarburst Enterprise
- Start Free
Fully managed in the cloud
Building these analytics repositories on commodity cloud platforms provides a more scalable, flexible, and affordable alternative to conventional data warehouse systems while giving users access to more varied data sources. This guide to object storage will explain what makes it such a powerful tool in today’s data analytics architectures.
Object storage is an alternative to traditional file systems for storing large amounts of unstructured data in scalable, cost-efficient, and performant storage architectures. This type of storage uses a flat structure to store an unlimited number of objects, each consisting of data values, metadata, and a unique identifier.
Cloud object storage implements this approach on commodity cloud storage services rather than on-premises systems. Object stores in the cloud are more dynamically scalable than a data center’s fixed infrastructure. As a result, object-based cloud storage is an increasingly common element of public cloud, hybrid cloud, and multi-cloud system architectures.
Related reading: Cloud-based object storage vs HDFS
Object storage is the optimal way to store data in a data lake since it can accommodate structured, semi-structured, and unstructured data within a single repository. The flat structure, unique identifiers, and object metadata let query tools rapidly find and access data at petabyte scales.
Let’s first look at other data storage methods to better understand how object storage works
Server and desktop operating systems use a hierarchical system of nested directories to organize data files. File protocols like the Windows operating system’s Server Message Block (SMB) or Linux’s Network File System (NFS) let users retrieve a file by following its path of directories and folders, whether files reside on internal storage devices or networked-attached storage (NAS) systems.
Hierarchical file storage is intuitive since it mirrors how people store paper files and other real-world objects. However, finding and retrieving a file saved in deeply nested folder structures takes time, which adds up when you’re talking about the huge data sets needed for business analytics.
Block storage systems split data files into smaller elements for efficient storage. This approach gets used in high transaction rate enterprise applications like a database management system where low latency is critical. The database uses a virtual file system on top of its physical storage. Each virtual file points to the addresses of its respective blocks. Retrieving a file is simply a matter of going directly to each address to gather the blocks and assembling them.
This flat structure lets block storage systems make the most efficient use of their physical storage devices. A file’s blocks don’t need to be stored together, letting the system save blocks wherever storage space is available.
Object storage systems save data in a flat structure, often called a storage pool or bucket. Similar to block storage’s unique addresses, object-based storage assigns each object a unique identifier. But it’s the metadata that makes object storage so effective for large-scale analytics.
Object metadata provides more information than in file-based or block-based systems. Object storage solutions can add tags to help enforce governance policies, describe data for faster indexing, and give query engines more discovery options for searching a data lake.
Traditional client-server architectures distribute files to clients in a request-and-response model. The server stores data within its hierarchical file system. Client applications use the server operating system’s file protocol to request a file. The server retrieves the file and sends it to the client.
An object store plays a similar coordinating role, acting as a central interface for one or more object storage solutions. Funneling access to object storage through a store streamlines data management and improves governance enforcement. A data store can also enhance data durability through replication, making the company’s storage architecture more resilient.
Making the best use of the volume, velocity, and complexity of data that businesses generate has become an enormous challenge. Conventional data warehouses work well with structured data. However, their fixed schema are incompatible with the variety of modern enterprise data sources like the Internet of Things (IoT) systems constant streaming real-time data.
As a result, data remains scattered in different locations and isolated in data silos. Finding data requires an engineer’s specialized skills and deep knowledge of the company’s storage infrastructure, leaving routine and ad hoc analysis requests to compete with big data analytics projects for scarce engineering resources.
Object storage solutions like a data lake let companies combine the output of disparate sources within a centralized repository to enable multiple storage use cases.
A data lake’s object store can be more accessible to a broader range of data consumers. Business intelligence analysts can query the lake’s deep, complex data ecosystem through tools like Tableau or Power BI. Automated dashboards can combine different types of data to inform decision-making.
Data lakes built on object storage are rich sources for the petabytes of data needed to train large-scale machine learning algorithms and artificial intelligence applications. Data scientists can explore disparate data sets and iteratively test their work. Object-based storage empowers advanced data analytics to create the deep insights that drive innovation.
Beyond analytics, objects are more effective ways to store backup and archival data. They work with any kind of data. Also, each object’s metadata makes it easier to find and retrieve, which is particularly useful for disaster recovery as it lets data management teams restore operations quickly. Object metadata also enables more efficient execution of data retention policies by quickly identifying data at the end of its lifecycle.
Object storage’s ability to store vast quantities of structured and unstructured data in efficient cloud-based storage services unlocks a host of benefits, including:
The biggest benefit of object-based storage is how it makes a data lake’s contents more accessible. Exploration and discovery are much faster than would be possible in file-based or block-based architectures, thanks to object metadata. Queries can find the right data sets in buckets with enormous data volumes without having to open and inspect the data itself.
As mentioned earlier, the rich metadata supported by object storage solutions can reinforce data governance practices. This metadata combines with access control rules to strengthen data protection, protect data privacy, and limit access to authorized users.
An object storage system’s flat structure is easier to scale. Adding more storage does not require changes to complex directory structures or file path names. As storage capacity increases, the data lake can begin writing objects. And that capacity can increase as much as necessary, accommodating the petabytes of data enterprises generate.
Companies can optimize object storage systems to meet their storage needs. Frequently accessed data can reside in a cloud provider’s high-performance, low-latency solid-state storage while other data remains on slower-spinning disks or in archival data systems.
Flexibility also enables the optimization of storage costs. Rather than treating all data the same, objects that generate the most value can live on expensive solid-state devices. Other objects get assigned to options with more affordable pricing, lowering the company’s overall storage costs.
We built Starburst to make the open source Trino query engine more accessible to teams implementing modern data lake architectures. Enhanced features extend Trino to make it a single point of access for all enterprise data. We support S3, GCS, Azure object storage systems as well as Tabular-based data stores running on S3.
You can integrate Starburst’s modern data lake analytics platform with cloud storage solutions like the Simple Storage Service (S3) offered by Amazon Web Services (AWS). You can use Starburst’s built-in Galaxy metastore, Amazon Glue, or the Hive Metastore Service to catalog object metadata and type mapping.
Starburst Gravity is our universal discovery, governance, and sharing interface. Automatic cataloging lies at the center of Gravity, pulling metadata from every data source to create a central hub for exploration and discovery. Gravity’s role-based and attribute-based access controls let you create granular rules governing access to every data object.
Great Lakes is Starburst’s connectivity feature, allowing you to integrate our analytics platform with Hive, Delta Lake, or Iceberg table formats to reduce expensive data moves and migrations significantly.
Starburst Warp Speed accelerates workloads through autonomous indexing and smart caching. Available for S3 and Tabular catalogs, Warp Speed dramatically improves query performance and reduces operational costs.
Up to $500 in usage credits included
Up to $500 in usage credits included