Remove Analytics Application Remove Architecture Remove Data Ingestion
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Handling Bursty Traffic in Real-Time Analytics Applications

Rockset

Lambda Architecture: Too Many Compromises A decade ago, a multitiered database architecture called Lambda began to emerge. Lambda systems try to accommodate the needs of both big data-focused data scientists as well as streaming-focused developers by separating data ingestion into two layers.

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Benchmarking Elasticsearch and Rockset: Rockset achieves up to 4X faster streaming data ingestion

Rockset

In scenarios involving analytics on massive data streams, we’re often asked the maximum throughput and lowest data latency Rockset can achieve and how it stacks up to other databases. For this benchmark, we evaluated Rockset and Elasticsearch ingestion performance on throughput and data latency. How did we do it?:

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Real-Time Data Ingestion: Snowflake, Snowpipe and Rockset

Rockset

Organizations that depend on data for their success and survival need robust, scalable data architecture, typically employing a data warehouse for analytics needs. Snowflake is often their cloud-native data warehouse of choice. Data ingestion must be performant to handle large amounts of data.

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Why Modernizing the First Mile of the Data Pipeline Can Accelerate all Analytics

Cloudera

Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis. By modernizing the data flow, the enterprise got better insights into the business.

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Unify your data: AI and Analytics in an Open Lakehouse

Cloudera

By leveraging the flexibility of a data lake and the structured querying capabilities of a data warehouse, an open data lakehouse accommodates raw and processed data of various types, formats, and velocities.

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Rockset Ushers in the New Era of Search and AI with a 30% Lower Price

Rockset

In 2023, Rockset announced a new cloud architecture for search and analytics that separates compute-storage and compute-compute. With this architecture, users can separate ingestion compute from query compute, all while accessing the same real-time data. minutes to batch load the data.

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Turning Streams Into Data Products

Cloudera

This blog aims to answer two questions as illustrated in the diagram below: How have stream processing requirements and use cases evolved as more organizations shift to “streaming first” architectures and attempt to build streaming analytics pipelines? Faster data ingestion: streaming ingestion pipelines.

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