Running analytic applications on the AWS Cloud has rapidly evolved from a specialty solution for social media startups to a key component of business strategy for forward-looking enterprises and savvy innovators. One by one, every industry is experiencing challenges as new and exotic analytics, visualizations, and datasets are introduced throughout the organization. The result is a critical need to reduce the overhead of designing and deploying new data warehousing projects for business intelligence (BI) and data exploration.
It’s no surprise that business leaders and IT professionals are flocking to the AWS Cloud to procure the compute power needed to gain value from data; to ensure scalability as the business (and the data) skyrockets; and to reduce the time, effort, and cost of their analytics efforts. The phenomenal rise of the AWS Cloud for a wide variety of workloads provides an opportunity for companies to host their most valuable datasets outside of their firewall to enjoy the safety, scalability, and savings of cloud storage. This has inspired companies like Looker to envision and deliver analytics capabilities that can live on the cloud or on-premises, while reducing or eliminating the inefficiencies and limitations of traditional data warehousing.
Businesses are increasingly dealing with important datasets both behind the firewall and on the AWS Cloud. There’s also been a paradigm shift away from standard data warehousing – the power and efficiency of databases now make it much more efficient to bring the analytics to the data, rather than vice versa, to leverage the underlying power of new data technology.
Check out a related article:
What are the key challenges that are transforming data warehousing?
- The high volumes and wide variety of data tend to break standard ETL processes and make the arduous steps of cleansing, organizing, and sub-setting large datasets impractical. They can also result in loss of fidelity and integrity of the source data.
- Ad hoc querying of the data warehouse (DW) by a select few BI specialists is a wasteful, exclusive approach to gaining insight from new data. Business leaders want BI at all levels and in all corners of their operations. Business users need access to all the data in their database and the ability to drill down to any level of granularity, which is impossible with the data cubes traditional DWs necessitate.
The Looker for Amazon Redshift Solution
While traditional DWs remain a mainstream BI use case in enterprise environments, the steady progress of cloud computing and big data techniques are pushing on-premises DW beyond its practical limits of scale, performance, and cost. Cloud scale, the massively parallel SQL processing of Amazon Redshift, combined with the rapid insights and scalable data modeling of Looker provides a necessary overhaul of the traditional BI/DW model.
Benefits of Looker for Amazon Redshift
Cloud Economics and Agile Analytics
Looker for Amazon Redshift addresses two of the major impediments to greater adoption of DW projects in the modern enterprise. First, the daunting capital expense of traditional DW is greatly mitigated by the economics delivered through AWS Cloud infrastructure and, more specifically, by the distributed MPP architecture of the Amazon Redshift solution.
Second, Looker delivers agility and immediacy by moving the analytics closer to the data and by providing integration with the advanced features of the database. Looker’s intuitive, engaging browser-based interface also lowers the cost of entry for getting real value from analytics—which, in turn, serves to engage and motivate your knowledge workers.
Move the Analytics, Not the Data
As the flood of big data from the cloud enters the BI landscape, IT departments are quickly coming to grips with the high cost of moving large datasets—and many of them are looking for a more economical alternative. The ubiquity and scale of Amazon Elastic Compute Cloud (Amazon EC2) infrastructure and the rich DW services of Amazon Redshift now present that alternative.
Check out a related article:
Looker leverages the advanced features of the Amazon Redshift solution with a powerful, streamlined approach to analytics. By querying directly, the underlying MPP database for only the data needed to answer a question, Looker provides the most efficient BI path in terms of hardware, storage, and compute power.
Scalable Intelligence for the Cloud
IT managers and business leaders have invested heavily in enterprise BI platforms, SaaS, and mobile applications to provide real-time insights for professionals across all lines of business. As new, powerful data services become available in the cloud, the combination of Looker and Amazon Redshift provides the basis for delivering insights at cloud scale.
Looker’s streamlined approach to getting value out of data wherever it lives—either on the cloud or on-premises—establishes a consistent analytical mechanism in hybrid cloud environments.
Go deep with Data Modeling
LookML, Looker’s built-in modeling layer, adds incredible depth to the immediacy of the Looker for Amazon Redshift solution, vastly increasing the power and range of questions you can ask of your data. A modern, browser-based development environment, LookML enables power users and data analysts to curate self-service exploration tools, easily changing or reusing the elements of the data model as the business demands.
Looker for Amazon Redshift is deployed within AWS Security Groups, so data security is consistent with your AWS environment, and no additional steps are needed to ensure that your data is not compromised.
Learn more about Looker here.