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ScyllaDB Trends – How Users Deploy The Real-Time Big Data Database

Scalegrid

ScyllaDB is an open-source distributed NoSQL data store, reimplemented from the popular Apache Cassandra database. We’ve heard a lot about this rising database from the DBA community and our users, and decided to become a sponsor for this years Scylla Summit to learn more about the deployment trends from its users.

Big Data 187
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Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. In the initial stage, data consumers set up ETL pipelines directly pulling data from databases.

Big Data 253
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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 243
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Mastering Hybrid Cloud Strategy

Scalegrid

Workloads from web content, big data analytics, and artificial intelligence stand out as particularly well-suited for hybrid cloud infrastructure owing to their fluctuating computational needs and scalability demands. Ready to take your database management to the next level with ScaleGrid’s cutting-edge solutions?

Strategy 130
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Fast key-value stores: an idea whose time has come and gone

The Morning Paper

Coupled with stateless application servers to execute business logic and a database-like system to provide persistent storage, they form a core component of popular data center service archictectures. A high CPU cost due to marshalling data to/from the RInK store formats to the application data format. Who knew! ;).

Cache 79
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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. There are several emerging data trends that will define the future of ETL in 2018. Machine learning meets data integration.