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Data Analytics: Server-less analysis using Google Cloud

Big Data Analytics can be used to analyze large amounts of data from a wide variety of sources. This allows patterns to be identified and new insights to be generated, which in turn supports the optimization of business processes.

Data analysis redefined

Companies have an almost infinite amount of data from the most diverse sources. Big Data technologies offer the opportunity to gain valuable insights from this data. Data analyses are costly and highly complex. Google Cloud Platform (GCP) provides the right environment to manage the complexity of data analysis. GCP not only makes it easy and fast to create statistics, but also provides integrated end-to-end, serverless data analysis services that are scalable, powerful and cost-effective.  

Focus on analysis instead of infrastructure 

GCP’s serverless approach eliminates operational overheads by automatically monitoring the performance, scalability, availability, security and compliance requirements of the big data analysis solution. This saves resources in two ways. First, it eliminates the need to manage servers and allows companies to focus on analysis. In addition, only the resources used are billed - depending on the service, e.g. per second or query. Flat-rate prices and discounts for continuous use are also available. As a Google Cloud Premier Partner, we know how companies can take full advantage of these benefits. 

Uncompromisingly accelerate development 

BigQuery, data in the gigabyte and terabyte range can be analyzed at lightning speed. REST-based APIs allow easy integration with other applications. Developers can create analysis applications in familiar programming languages such as Java, Python, C#, Go, Node.js, PHP or Ruby. With Cloud Pub/Sub, they can record millions of events per second from anywhere via an open API and publish them anywhere.  Cloud Dataflow accelerates pipeline development for streaming and batch data without compromising stability, accuracy or functionality. A useful addition to BI solutions are fast dashboards in Data Studio.

Use tools more efficiently with open architecture

The open architecture of GCP is based on the use of open source tools, which are offered as a managed service. This accelerates value creation and prevents vendor lock-in. By switching to Cloud Dataproc The integration of popular open source tools such as Apache Kafka as Cloud Pub/Sub or the processing of data pipelines with Cloud Dataflow based on Apache Beam work with different open source runtimes. 

Cloud Data Fusion enables managed data integration via a graphical user interface and a comprehensive open source library with pre-configured connectors and transformations based on CDAP. Cloud Composer, built on Apache Airflow, simplifies workflow orchestration for public clouds and local environments. Use familiar BI and visualization tools such as Tableau, Qlik,, Data Studio, and BigQuery BI Engine to easily share information.

Protect the enterprise and its data

Regardless of workload, GCP provides a secure, scalable and reliable infrastructure in which mission-critical data analysis solutions can be created and maintained. Google’s multi-layered security approach provides redundancy and high reliability. According to the terms of use, all data analysis products have an availability of over 99% (Service Level Objective, SLO). Organizations protect and control data in the Google Cloud with industry-leading tools and technologies that also support key compliance requirements: Cloud Identity & Access Management (IAM) for granular identity and access management, Cloud Data Loss Prevention for classifying and removing sensitive data, and Data Catalog for metadata management and data discovery.

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