Remove Aggregated Data Remove Events Remove MongoDB
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Comparing ClickHouse vs Rockset for Event and CDC Streams

Rockset

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Event streams, such as clickstreams, IoT data and other time series data, are common sources of data into these apps. ClickHouse has several storage engines that can pre-aggregate data.

MySQL 52
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What is a Data Pipeline (and 7 Must-Have Features of Modern Data Pipelines)

Striim

Striim supported American Airlines by implementing a comprehensive data pipeline solution to modernize and accelerate operations. To achieve this, the TechOps team implemented a real-time data hub using MongoDB, Striim, Azure, and Databricks to maintain seamless, large-scale operations.

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Case Study: Is Your NoSQL Data Hindering Real-Time Analytics? Savvy Solved It with Rockset.

Rockset

All interactions are streamed in the form of semi-structured events into Firebase’s NoSQL cloud database, where the data, which includes a large number of nested objects and arrays, is ingested. We ended up deploying a real-time analytics platform, Rockset , on top of MongoDB. It feels like magic!

NoSQL 52
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How Rockset Enables SQL-Based Rollups for Streaming Data

Rockset

A Quick Primer on Indexing in Rockset Rockset allows users to connect real-time data sources — data streams (Kafka, Kinesis), OLTP databases (DynamoDB, MongoDB, MySQL, PostgreSQL) and also data lakes (S3, GCS) — using built-in connectors. You can also optionally use WHERE clauses to filter out data.

SQL 52
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Data Lake vs. Data Warehouse: Differences and Similarities

U-Next

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. In the event that they are not the same, what are the difference s? Gen 2 Azure Data Lake Storage . Data lakes can also be organized and queried using other technologies, such as .

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Python for Data Engineering

Ascend.io

Use Case: Transforming monthly sales data to weekly averages import dask.dataframe as dd data = dd.read_csv('large_dataset.csv') mean_values = data.groupby('category').mean().compute() compute() Data Storage Python extends its mastery to data storage, boasting smooth integrations with both SQL and NoSQL databases.

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The Good and the Bad of Apache Kafka Streaming Platform

AltexSoft

This scenario involves three main characters — publishers, subscribers, and a message or event broker. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker. Kafka cluster and brokers.

Kafka 93