Big Data Technologies

In Amazon Web Services

AWS offers an ideal environment to implement Big Data on the public cloud. It provides multiple core and specific services that can do so with tight time-to-market.

AWS ELASTIC MAP REDUCE

Processing large volumes of data quickly and easily using frameworks such as Spark, Presto, HBase or Flink.

AWS LAMBDA

Lets you run code without provisioning or managing servers. Pay only for what you use and enjoy native integration with other AWS services.

AWS KINESIS

Like other AWS services, it is fully managed and easily scalable. It lets you collect, process and analyze date flows in real time.

AWS REDSHIFT

Database specialized in storing and processing large quantities of information, with superior performance to conventional databases thanks to its machine learning and parallel processing capabilities.

AWS SIMPLE STORAGE SERVICE

Object storage service that can be used as a Data Lake in different architectures, thanks to its characteristics and integration with other AWS services.

AWS QUICKSIGHT

Managed service to visualize the data on different storage systems. It can scale from 10 to 10,000 users with no need to manage servers.

Why Amazon Web Services for data projects?

Thanks to wide range of services it offers, AWS has become the ideal environment to design and implement Big Data projects tailored to your business needs. AWS has multiple sources of information that help you make the transition quickly, simply and only paying for what you use, which is especially suitable for projects like reference architecture, technical documents and training courses.

Keepler: AWS experts

With 90% of our technical specialists certified in AWS, including specific Big Data certifications, our architects have seen AWS advance and change from its very beginnings, giving them deep knowledge and expertise with the platform.

AWS Cloud Benefits

PAY PER USE

Separation between computing (EMR) and storage (S3) in Data Lakes, allowing for separate cost increases instead of provisioning a single cluster.

SCALABILITY

Possibility to provision any number of Hadoop clusters of any size, either on demand or for Redshift in interactive analytics.

FLEXIBILITY

Ability to use Apache Hadoop technologies (Hive, Pig, Spark, Impala, etc.) or SQL query languages with Redshift and Athena.

GREAT PERFORMANCE

The infrastructure features nodes specialized in computing, memory or network, giving it the ability to respond to any performance demand.

SECURITY

Access and execution of data processes using roles for requests and security groups for network access.

HIGH AVAILABILITY

The data are always available thanks to services like S3 and DynamoDB, with an optimal service commitment.

FAULT TOLERANCE

Components have the ability to recover from service interruptions, even with characteristics such as job attempts.

MANAGED SERVICES

Managed services such as S3, Athena and Quicksight with no management by operations.

If you want to make the move to the AWS public cloud, contact us and we’ll talk.

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