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Unlock the Power of Cloud-Native Databases: Modernize Your Data Management Today

Storage isn’t always top of mind when we think of modernization, but leveraging data lakes and cloud-native databases can be a game-changer for organizations. This can help to better classify data, enforce security and privacy controls, and build a foundation for advanced analytics. A data lake is a logical next step when looking to modernize your data. This serves as a central repository for landing raw data, including both relational data (from business applications) and non-relational data (from mobile apps and social media). In a data lake, data can be stored as-is, while cloud-based databases can then turn that raw data into actionable insights.

Challenges with legacy storage systems

Data is growing exponentially—and much of that data is unstructured. On-premise storage must be continually upgraded, provisioned and managed to meet this growing demand. Data stored on-premise or in disparate locations can make it difficult to leverage that unstructured data for more advanced applications, such as analytics and artificial intelligence. Security is another major issue. Fine-grained access control allows organizations to enforce user consents, enable data security and comply with data privacy regulations. But providing fine-grained access is difficult (and costly) with legacy data stores, which could open up your organization to security and privacy risks.

 

Taking the next step: Storing data in the cloud

Cloud-based storage is highly scalable, secure and redundant. Unlike legacy solutions, security and backup are unified in the cloud. Protecting against a ransomware attack, for example, is made easier with redundancy and backup in the cloud combined with advanced security features. If you’re not ready to move everything over to the cloud, you can explore a hybrid solution, with some data stored on-premises and some in the cloud. Most cloud providers also offer automated controls over data storage and management. Consider the possibilities:

  • Data that hasn’t been accessed for a certain period of time could automatically be moved to a lower tier of storage that’s more cost-effective.
  • Automation can define policies for how data is secured, how often it’s backed up and how long it’s retained.
  • Permissions settings can control who can access what data, and what they can do with it.

Perhaps most importantly, many cloud solutions offer pricing models where you only pay for what you need—and it’s easy to scale up or down as your needs change over time.

Cloud-native databases: Make the most of your data

A data lake brings all of your data together into a centralized repository, allowing you to store raw data—whether structured or unstructured—at scale. From there, you can load data into a multitude of different cloud-native databases that provide users with access to data in the format and structure they want, with the performance they require. Cloud-native databases leverage flexibility, scale and significant computing power so you can get the most out of your data lake. The platform and functionality you choose will depend on what your data looks like, and the type of insights you want it to inform. Some of these options include:

Google BigQuery storage is optimized to run analytic queries over large datasets with its next-gen columnar storage format. It separates storage and compute so they can scale independently—and you can optimize your workloads. BigQuery storage is fully managed, which means it automatically allocates storage when data is loaded into the system and you only pay for what you use. Data is also automatically encrypted and replicated across multiple availability zones for data loss protection.

Google Cloud Storage is a managed service for storing unstructured data, such as images, audio, video and free-form text. It allows you to store any amount of data and retrieve it as often as you like, based on the type of data. Once your data is stored in Cloud Storage, it’s part of your Google Cloud ecosystem, so you can use it as a foundation for analytics, machine learning and artificial intelligence.

Elasticsearch is capable of storing complex data structures. Rather than storing data in rows of columnar data, data structures are serialized as JSON documents. Essentially, this means a stored document is indexed and fully searchable in near real-time—as fast as one second.

Managing storage in the cloud

Cloud-native databases come with an array of security, privacy and data management features, which require configuration and customization to protect your data effectively. That could involve configuring corporate retention policies, building team-specific permissions settings or designating a retention period for different tiers of data. Many businesses also operate in a hybrid or multi-cloud environment. They might have data stored across multiple hyperscale clouds, or in a hybrid environment of on-premise legacy systems and cloud storage. The more complex the environment, the harder it is to manage—and the more vulnerable it is to security risks.

Expand your cloud capabilities

If your team is ready to migrate to the cloud, but lacks the capacity or capability to manage the intricacies involved, we’ve got you covered. At Pythian, our certified Google Cloud and data management experts can help you every step of the way, from migrating to the cloud to integrating it with your broader environment—so you can get to value faster. Get in touch with a Pythian Google Cloud expert to see how our team can help.

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