![]() ![]() In this blog, I am going to look at three high-level data ingest scenarios and the simple, repeatable AWS services that you can use as data ingest and transfer mechanisms for each scenario: What AWS data transfer options are available? It is paramount to ensure that the data you copy across to the target is the same as it was on the source.Īs you can see, this can quickly get complicated and can become a time-bound exercise. Then, determining who is going to maintain it, and re-write it for different use cases with the aim of making it a repeatable mechanism is challenging.Ĭomplexity: It can get complex trying to create code to perform functions such as preserving file metadata between source and target, or performing post data transfer integrity verification. Time and effort: The time and effort required in building and testing bespoke data transfer code. Speed: What’s the fastest and simplest way to transfer data, and which method can seamlessly scale to meet varying volumes and dataset characteristics, such as small file vs large file transfers, for optimal transfer speeds? Options: There are many options to consider should you transfer data online over the wire, or offline, or a combination of both using a seed and sync approach? Which option is optimal for your use case, data transfer timelines, and fits into your business process? Potential challenges when ingesting data into AWS This enables customers to move away from aging, costly, and complex on-premises storage infrastructure to leverage greater cost savings and operational efficiency. Sharing data: Customers are increasing productivity and business value by sharing data, at local and global scale.īackups: Customers are storing highly durable and cost effective backups of their data in Amazon S3, to meet business and compliance requirements.Īrchiving data: There is an ever growing amount of data, and customers are archiving their long-term retention-based data to AWS. With these services, customers can increase agility and operational efficiency. By doing so, they can then obtain greater value out of their aggregated data.Īpplication data migration: Customers migrate application data into AWS so that they can leverage the benefits of fully managed AWS file services, such as Amazon EFS and Amazon FSx for Windows File Server. Why customers transfer data into the AWS Cloudĭata lakes: Customers are ingesting and centralizing data into the highly available, scalable Amazon S3 storage service to build data lakes and centralize data processing capabilities. In addition, I supply links to hands-on workshops that provide guides for deploying, configuring, and transferring data using these services. I also provide demo videos showing the AWS services in action, so you can visualize their benefits. In this blog, I cover some of the key AWS Storage services that can help you understand the options you have available for your data transfer requirements. Customers want to move away from the complexity of scripts, and custom tooling, to more repeatable design patterns, enabling them to focus their resources on innovation where it matters most to their business. When speaking with our customers, they often look to AWS to invent and simplify optimal data transfer mechanisms to help them on their data migration journey to AWS. ![]() Take, for example, customers looking to seed their data lakes with historical data so they can enrich their machine learning models and enable greater accuracy for future predictions. When customers think of adopting new data storage services to meet requirements like cost and performance optimization, scalability, and operational efficiency, there is usually an element of data migration involved. Increasingly, customers are also seeking to enable integration paths for their data so they can do more with it, such as building data lakes to gain deeper insights through analytics. Customers are looking for cost optimized and operationally efficient ways to store and access their data. The sources of this data range from traditional sources like user or application-generated files, databases, and backups, to machine generated, IoT, sensor, and network device data. An increasing amount of data is being generated and stored each day on premises. ![]()
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