Companies rely on data to make important decisions, but an outdated method of data storage can slow down access to this information. A traditional data warehousing approach would store this data on-prem, where storage and processing capacity has little room to grow. Furthermore, the on-prem hardware on which your database runs can fail, driving up costs and disrupting business operations.
The introduction of cloud computing has quickly solved these challenges. The cloud offers the same level of access to hardware as on-prem while reducing the burdens associated with infrastructure management. With improved security, reliability and performance, the cloud is the next logical step for enterprises that store and rely on large datasets.
The cloud has emerged as a boon for enterprise computing but the number of choices on offer can add complexity to your database migration initiatives. Each enterprise will have its computational requirements, meaning no single approach will work for every business.
We understand the importance of business diversity in the market. This vibrancy should not prevent you from accessing the best storage technologies, which is why our consulting services can help you find the optimal choice for your business.
When moving your database to the cloud, there are many avenues to consider, including:
Self-managed databases – Most businesses consider lifting and shifting their database to the cloud and self-manage it. With this approach, you simply pay for access to a remote server with database development and configuration being left to your IT department.
This approach is suited to enterprises that need granular control over their database or forecast a linear trajectory for dataset growth over time. In this case, you would pay for Infrastructure-as-a-Service (IaaS) without any additional management.
Database-as-a-Service – Database-as-a-Service (DBaaS) is a type of managed database service. This approach still utilizes IaaS as a foundation but reduces the requirement for hands-on management further with additional software monitoring and automation. DBaaS is also platform-agnostic, allowing you to link databases from multiple cloud platforms and manage them with a central abstraction layer.
With DBaaS, there are many benefits. The biggest and most immediate benefit is that the database is managed for you alongside the hardware, reducing workloads for IT departments. For enterprises that need reliable database hosting without the complexity of management, DBaaS may be the best option.
On top of these cloud database strategies, you also need to choose a hosting platform for your database. There are numerous options from major and specialist providers, including our partners:
Amazon Web Services (AWS) – Trianz has partnered with AWS, one of the biggest cloud hosting providers. Amazon Aurora provides managed, optimized MySQL, and PostgreSQL RDBMS hosting. If you want an unmanaged service, Amazon S3 provides a simple storage service for object-driven databases.
Microsoft Azure – Trianz is a partner with Microsoft Azure and provides database hosting services on its platform. For unmanaged databases, Azure Blob storage offers scalable object-based storage in the cloud. You can also choose a managed service through the Azure Database for MySQL, PostgreSQL and Microsoft SQL Server.
Snowflake – We have also partnered with Snowflake, a managed DBaaS provider. Snowflake leverages virtual compute instances on AWS, Azure and GCP, meaning they control both the hardware and software for your database.
If your business operations are starting to outgrow your on-prem data center, consider making a move to the cloud. We have helped to orchestrate datacenter migrations for thousands of businesses, including numerous Fortune 1000 companies. Our expertise and experience with the cloud allows us to transform your database deployment, catalyzing business growth and reducing workloads for your IT department.
Our consultants are only a few clicks away. If you are ready to start benefitting from the cloud, get in touch with our experts!
Contact Us Today
For decades, Windows served as the workhorse of the business world. In recent years, however, a significant transformation has occurred with the rise of cloud infrastructure platforms. Enterprises now realize that legacy on-premises Windows workloads are impeding their progress. Core challenges include licensing costs, scalability issues, and reluctance to embrace digital transformation.Explore
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore