Wednesday, July 18, 2018

Why does cloud make Data Lakes better?

A data lake is conceptual data architecture which is not based on any specific technology. So, the technical implementation can vary technology to technology, which means different types of storage can be utilized, which translates into varying features.
The main focus of a data lake is that it is not going to replace a company’s existing investment in its data warehouse/data marts. In fact, they complement each other very nicely. With a modern data architecture, organizations can continue to leverage their existing investments, begin collecting data they have been ignoring or discarding, and ultimately enable analysts to obtain insights faster. Employing cloud technologies translates costs to a subscription-based model which requires much less up-front investment for both cost and effort.

The most of the organizations are enthusiastically considering cloud for functions like Hadoop, Spark, data bases, data warehouses, and analytics applications. This makes sense to build their data lake in the cloud for a number of reasons such as infinite resources for scale-out performance, and a wide selection of configurations for memory, processors, and storage. Some of the key benefits include:
  1. Pervasive security - A cloud service provider incorporates all the aggregated knowledge and best practices of thousands of organizations, learning from each customer’s requirements.
  2. Performance and scalability - Cloud providers offer practically infinite resources for scale-out performance, and a wide selection of configurations for memory, processors, and storage.
  3. Reliability and availability - Cloud providers have developed many layers of redundancy throughout the entire technology stack, and perfected processes to avoid any interruption of service, even spanning geographic zones.
  4. Economics - Cloud providers enjoy massive economies of scale, and can offer resources and management of the same data for far less than most businesses could do on their own.
  5. Integration - Cloud providers have worked hard to offer and link together a wide range of services around analytics and applications, and made these often “one-click” compatible.
  6. Agility - Cloud users are unhampered by the burdens of procurement and management of resources that face a typical enterprise, and can adapt quickly to changing demands and enhancements. 
Advantages of a Cloud Data Lake – it is already proved that a data lake is a powerful architectural approach to finding insights from untapped data, which brings new agility to the business. The ability to harness more data from more sources in less time will directly lead to a smarter organization making better business decisions, faster. The newfound capabilities to collect, store, process, analyze, and visualize high volumes of a wide variety of data, drive value in many ways. Some of the advantages of cloud data lake is given below –
  • Better security and availability than you could guarantee on-premises
  • Faster time to value for new projects
  • Data sources and applications already cloud-based
  • Faster time to deploy for new projects
  • More frequent feature/functionality updates
  • More elasticity (i.e., scaling up and down resources)
  • Geographic coverage
  • Avoid systems integration effort and risk of building infrastructure/platform
  • Pay-as-you-go (i.e., OpEx vs. CapEx)
A basic premise of the data lake is adaptability to a wide range of analytics and analytics-oriented applications and users, and clearly AWS has an enormous range of services to match any. Many engines are available for many specific analytics and data platform functions. And all the additional enterprise needs are covered with services like security, access control, and compliance frameworks and utilities.
Please visit us to learn more on -
  1. Collaboration of OLTP and OLAP systems
  2. Major differences between OLTP and OLAP
  3. Data Warehouse - Introduction
  4. Data Warehouse - Multidimensional Cube
  5. Data Warehouse - Multidimensional Cube Types
  6. Data Warehouse - Architecture and Multidimensional Model
  7. Data Warehouse - Dimension tables.
  8. Data Warehouse - Fact tables.
  9. Data Warehouse - Conceptual Modeling.
  10. Data Warehouse - Star schema.
  11. Data Warehouse - Snowflake schema.
  12. Data Warehouse - Fact constellations
  13. Data Warehouse - OLAP Servers.
  14. Preparation for a successful Data Lake in the cloud
  15. Why does cloud make Data Lakes Better?


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