Storing data in the cloud has become more enticing to enterprises. Not only have prices gone up for on-premises data storage, but it can be a nightmare for IT teams to maintain the infrastructure needed to securely store growing volumes of structured and unstructured data.
AWS offers a variety of services to store, manage and analyze data. More importantly, AWS provides security and encryption options to protect data at rest and in transit. And each new product launch adds to the ever-expanding AWS data management portfolio.
IT teams need to keep pace with the twists and turns of the cloud giant's technology. While some enterprises still have concerns over data privacy and security in the cloud, AWS has made many attempts over the years to draw in those customers by assuaging their security fears while offering a more cost-effective option to on-premises hardware. In this essential guide, we explore different AWS data management services and options.
1Pump data into AWS-
Data migration and storage
Enterprises new to AWS will want to research -- and, potentially, experiment with -- the cloud and its capabilities. This helps administrators decide which data should move to the cloud -- and in which region it should be stored. Additionally, admins must carefully determine a security agenda and approach to data storage. After selecting their ideal methodology, they can migrate data to the cloud and choose between several AWS database options. At re:Invent 2015, AWS continued its focus on the enterprise, introducing a variety of new data migration services. AWS Import/Export Snowball, for example, is a physical storage appliance for receiving and shipping large-scale data. The AWS Database Migration Service and AWS Schema Conversion Tool allow businesses to migrate and convert existing databases to another format, if desired. Amazon DynamoDB is an oft-used managed NoSQL database, and AWS also offers Amazon Relational Database to fit MySQL, PostgreSQL, Oracle and MariaDB needs. Amazon Simple Storage Service (S3) is the baseline AWS data management product, offering different object storage access tiers and thorough integration with other AWS products. AWS provides options for cold, block and file system storage, suiting the many needs of its customers.
2Use tools to keep data safe-
Data encryption and security
AWS adheres to a shared responsibility security model. This means the cloud provider does what it can to secure its infrastructure and supply security tools, but customers must work to address application vulnerabilities. One way to secure data is through encryption. The AWS Key Management Service enables enterprises to manage the encryption keys or let AWS handle that process -- rendering data unreadable to anyone other than the administrator in both cases. Amazon Identity and Access Management (IAM) restricts access to files and resources, depending on roles set by cloud admins, which can further prevent data from falling into the wrong hands. Admins can establish roles with IAM and set policies with other services, such as protecting S3 buckets and using them to control encryption in motion. IT teams must evaluate the security needs of their businesses and customers and make sure AWS can accommodate those needs. AWS has a variety of compliance certifications, but customers with particularly sensitive data might choose to keep some or all of it on premises.
3Make use of big data-
Big data analytics
The problem with data in an increasingly technology-driven world is that it can come in many formats and in large sizes. Yet, there's value in that data. And businesses increasingly seek to mine details from data -- no matter how cumbersome. AWS has cornered the public cloud market in part due to functionality between its services. APIs connect AWS products, allowing data to be processed with ease. Relying on the strengths of its network, Amazon has beefed up its big data analytics offerings. Internet of Things (IoT) is gaining market viability, but interoperability can be a difficult task for small businesses to operate on premises. AWS' IoT platform can help enterprises boil down real-world information into actionable data. In addition, AWS offers BI products to fit a range of needs. Amazon QuickSight visualizes data processed in the cloud, while Amazon Machine Learning handles complicated calculations to predict future end user behavior based on past days. Amazon Elastic MapReduce even offers a framework for the Java-based Hadoop, which processes large data sets. For data capture, Amazon Kinesis Firehose works in conjunction with S3 and Amazon Redshift, a managed data warehouse, to absorb that information to the cloud for processing.
Surveys show big data analytics projects are on the rise, which puts AWS in a good position to take advantage of the demand. Continue Reading
AWS Machine Learning offers powerful predictive capabilities and can help spot ongoing issues, such as fraudulent transactions and data center equipment failures. Continue Reading
4Helpful data terms-
Dealing with data can uncover some foreign terms for those not already familiar with the cloud. These definitions help expand your cloud data vocabulary.