Best Data Lake Solutions for Apache Cassandra

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Varada's adaptive and dynamic big data indexing solution allows you to balance cost and performance with zero data-ops. Varada's big data indexing technology is a smart acceleration layer for your data lake. It remains the single source and truth and runs in the customer's cloud environment (VPC). Varada allows data teams to democratize data. It allows them to operationalize the entire data lake and ensures interactive performance without the need for data to be moved, modelled, or manually optimized. Our ability to dynamically and automatically index relevant data at the source structure and granularity is our secret sauce. Varada allows any query to meet constantly changing performance and concurrency requirements of users and analytics API calls. It also keeps costs predictable and under control. The platform automatically determines which queries to speed up and which data to index. Varada adjusts the cluster elastically to meet demand and optimize performance and cost.