Aggregate your business’ data into a data lake and run machine learning workflows to extract meaningful insights.
We can help you deploy a data warehouse that can store petabytes of structured data across many sources in your enterprise. Data warehouses are usually filtered, structured data that is stored in a column-oriented database.
We also have experience deploying data lakes, where raw data is aggregated in a central storage location, and can be queried to extract relationships or patterns. As opposed to a data warehouse, a data lake is usually raw, unfiltered, and unstructured data.
We can help you build complex ingestion workflows (including Kafka and Kinesis) where data is received simultaneously from hundreds to millions of devices (such as log, or IoT devices), then processed and finally stored in a data lake or warehouse.
Hadoop is a data management platform that is composed of multiple servers in a cluster that can stored unstructured, non-relational data. A Hadoop cluster is one technology that can be used to build a data lake.
We can help you design, train, and deploy complex AI and machine learning architectures using Tensorflow and Pytorch. We can also help you deploy serving containers to run inference on your data and to extract patterns.
Spark is a general-purpose cluster-computing framework that can run machine learning and analytics models by distributing its workload across a cluster of nodes. We can help you use Spark to perform ETL workloads, and to query big data at scale.
We also experience building near real-time streaming analytics pipelines on Kinesis and Kafka. For instance, you could run an anomaly detection model directly on incoming IoT data.
Finally, we can help you build real-time dashboards to drill down on KPI from your data in a data warehouse and data lake. This can enable you to visualize what is going on throughout your enterprise.
Introduces BigQuery, a fully-managed and cloud-based interactive query service for massive datasets
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