Architecture and design of Data solutions
A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. The AWS services allow you to build and operate a modern Data Warehouse at scale, our consultants will guide you on this.
With AWS you can build highly scalable and secure Big Data applications fast, based on Hadoop and Spark technologies. No hardware to procure, no infrastructure to maintain, everything is serverless on AWS !
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
Our reference architecture
AI & ML expertise
Amazon SageMaker is built on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
AWS pre-trained Artificial Intelligence tools
Data & Big Data services
At Lucy in the Cloud, we have a front-row view of all the ways that Redshift can be used to help businesses manage their data. It is a versatile AWS product that can help businesses aggregate, store, analyze, and share their data.
A key advantage of Redshift is simplicity, it’s even available as a full serverless service now. It used to take months to get a data warehouse up and running. None of that anymore! You can spin up a Redshift cluster in less than 15 minutes, and build a whole business intelligence stack in a couple of days using Amazon Redshift.
Amazon EMR is a cloud Big Data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto.
Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale. DynamoDB offers built-in security, continuous backups, automated multi-region replication, in-memory caching, and data export tools.
Apache Kafka is a distributed data store optimized for ingesting and processing streaming data in real-time. Streaming data is data that is continuously generated by thousands of data sources, which typically send the data records in simultaneously. Our experts master this technology which is rapidly becoming a standard to deploy scalable, multi sources ingestion patterns.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information.
With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications.