Choosing your cloud solution, tools and technological trends for data platform migration
Discover the technological tools, current trends and best practices for optimizing a data platform in the cloud and exploiting its full potential.
Choosing a cloud provider is a key step in migrating data to the cloud. Indeed, the implementation of a cloud solution must respond to performanceissues and be adapted to the company's specific needs.
Leading cloud providers, such asAWS, Microsoft Azure and Google Cloud, offer a variety of offerings, each with its own strengths and specializations. To ensure optimal cloud migration and operation, it's essential to evaluate several criteria: compatibility with existing systems, security certifications, data localization and operating costs.
In this guide, we'll analyze the technological tools, emerging trends and practices you need to implement to take full advantage of a cloud-based data platform.
- Select the right cloud solution: AWS, Azure and GCP offer services tailored to your needs in terms of performance, security, analytics, AI...
- Explore native cloud solutions: Snowflake (optimized data warehousing), Databricks (big data and machine learning) and microservices optimize data processing.
- Follow trends to optimize cloud solutions: Multi-cloud, Edge computing, migration automation and data sovereignty are all influencing enterprise cloud strategies.
A reminder of the fundamentals of migration
Choosing your cloud solution: which cloud provider?
Microsoft Azure Cloud Solution
Amazon Web Services (AWS) Cloud Solution
Cloud solution Google Cloud Platform (GCP)
Choosing cloud solutions to optimize your migration to the cloud
Once the migration to the cloud is complete, the choice of tools becomes essential to fully exploit the power of the platform. Different solutions exist for storing, processing and analyzing data, each meeting specific needs. Whether we're talking about analytical platforms, data warehouses or solutions dedicated to machine learning, it's crucial to opt for the technologies best suited to your company's objectives.
Leading solutions such as Databricks and Snowflake offer complementary approaches to structuring, processing and analyzing data efficiently in the cloud.
Snowflake, a cloud-native solution for simplified data management
Snowflake is a fully cloud-native SaaS platform designed specifically for data warehousing, integration and analysis. What sets Snowflake apart is its ability to simplify data management while offering significant analytical power. Thanks to its unique architecture, Snowflake manages both structured data (such as relational databases) and semi-structured data (such as JSON, XML or Parquet) on the same platform. This flexibility makes it an ideal solution for companies seeking to centralize and analyze data from multiple, heterogeneous sources.
When it comes to migrating data to the cloud, Snowflake eases the transition with intuitive tools that enable data to be quickly transferred from existing environments (on-premise or other clouds). Its fully managed approach frees users from infrastructure management tasks, enabling teams to concentrate on data analysis and decision-making. Another strong point of Snowflake is its ability to share data securely and in real time between different teams or departments, making data access smooth and collaborative.
Snowflake is thereforea solution for companies looking to simplify their data management, while harnessing advanced analytical capabilities, without having to worry about the technical aspects of the underlying infrastructure.
Databricks, a cloud platform for massive data processing and machine learning
Databricks is a PaaS platform designed for processing large amounts of data and running large-scale machine learning projects. While Snowflake stands out for its simplicity and accessibility, Databricks is aimed more at companies requiring high computing power to run complex analyses and artificial intelligence (AI) models in real time.
Databricks offers parallel processing capabilities, enabling massive volumes of data to be processed extremely quickly and efficiently. The platform also supports Databricks Delta, a powerful tool for real-time data management, facilitating the ingestion, transformation and analysis of data as it is generated. The solution therefore enables the creation of complex data pipelines and the execution of machine learning models directly within the platform environment, without the need to move data around.
However, Databricks requires advanced technical skills, particularly in programming and data pipeline management. Companies choosing this solution must ensure they have a team of data engineers and data scientists capable of exploiting the platform's full potential. Unlike Snowflake, which offers a more accessible interface for data analysts, Databricks is better suited to users with development and massive data processing skills.
Current strategies and trends
Rapidly evolving technologies and business needs are driving companies to rethink their infrastructure and data management strategies. Against this backdrop, a number of trends are emerging to address the complex challenges of migrating to the cloud and optimizing data environments.
The Multi-Cloud way
The question of multi-cloud may arise during a migration strategy. While this approach may seem attractive for avoiding dependence on a single supplier, it involves significant trade-offs. Each additional cloud multiplies the need for expertise, complicates governance and reduces economies of scale - both in terms of commercial discounts and resource optimization. A more pragmatic approach is to choose a core provider while exploiting the specific strengths of other platforms: for example, AWS for application and analytics workloads, complemented by GCP for particular needs in artificial intelligence or massive data management.
In addition, there are cases where organizations adopt a hybrid cloud approach, where one management or department uses a specific cloud (for example, marketing on GCP), while other teams pursue their projects in an on-premise cloud. This model makes it possible to meet the specific needs of each department, while maintaining a certain consistency in the overall infrastructure.
On the other hand, some critical applications must remain on-premise for reasons of security, compliance or performance.
Finally, for companies operating in regulated sectors, there are additional considerations such as security certifications and compliance standards, like SecNumCloud, or HDS.
Other factors influence the choice of supplier, such as data localization constraints (e.g.: sensitive data that cannot leave a specific geographical area) or latency, where local Edge providers can be used to meet real-time processing needs.
It is also important to take into account specific costs, such as egress fees, which can considerably increase operating costs, as well as data archiving costs.
Migration automation and orchestration
Cloud migration management is becoming increasingly automated, thanks to sophisticated tools and platforms that simplify and accelerate this complex process. Automation reduces the risk of human error and ensures a smooth transition, with no service interruptions.
Solutions such as AWS Migration Hub, Google Cloud Migrate or Azure Migrate offer real-time planning and monitoring capabilities, enabling companies to track the status of their migration and quickly identify any blockages.
These tools also integrate orchestration mechanisms to automate tasks such as data replication, cloud resource configuration and security setup. Automated migrations help to increase efficiency and reduce the time needed to transition workloads to the cloud, while ensuring better management of dependencies between applications and infrastructures.
Native cloud and microservices
The adoption of native cloud architectures and microservices is a growing trend in the design of some modern solutions, particularly for applications requiring high flexibility and scalability.
Unlike monolithic applications, which rely on centralized code that is difficult to scale, microservices break down applications into small, independent functional units. Each microservice can be deployed, updated and managed autonomously, enabling greater scalability and agility in the cloud.
This model makes applications more flexible and resilient, as each microservice can be adapted independently of the others to meet specific needs. It also enables optimal management of cloud resources, as each component can evolve according to its own scale. For example, a company can increase the capacity of a microservice linked to transaction processing without having to resize the entire application. This model also favors a DevOps approach, enabling continuous and rapid updates while reducing time-to-market for new features.
Edge Computing
The phenomenon is becoming increasingly central in certain industries, where speed of execution and reduced latency are crucial.
Rather than moving all data to a data center or the cloud for processing, edge computing involves processing part of the data close to its source, i.e. at the edge of the network, directly on local devices or servers close to end users. This significantly reduces latency and optimizes performance for real-time applications, such as those used in the IoT, autonomous cars or health monitoring devices.
This approach is particularly relevant for sectors where decisions need to be made instantly. By combining edge computing with cloud technologies, companies can benefit from a hybrid infrastructure capable of processing critical data locally, while leveraging the computing power of the cloud for deeper analysis.
Data Fabric and data integration
The Data Fabric is a key concept in modern data management, facilitating data integration and governance within hybrid and multi-cloud environments. It represents a flexible, scalable architecture that seamlessly connects data sources, regardless of their location (on-premise, in the cloud or at the edge).
A Data Fabric enables data to be integrated and secured centrally, while providing unified access to information. This approach promotes consistent data management, simplifies integration processes and improves visibility of data flows. It enables companies to overcome the complexity of managing data dispersed across heterogeneous environments, while guaranteeing security and compliance.
Data sovereignty
Data sovereignty has become a major concern for companies in the face of increasingly stringent regulations on data localization. Legislation such as the General Data Protection Regulation (GDPR) in Europe or local laws in Asia, North America and elsewhere require certain sensitive data to be stored and processed in specific jurisdictions.
This geographical constraint is driving companies to adopt multi-cloud strategies and leverageedge computing to ensure that data remains compliant with local requirements. Cloud providers now offer solutions tailored to this issue, with options for choosing where and how data is stored and processed, while complying with current legislation.
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