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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. The Reporting View , which displays charts with aggregate data on visitors such as number of visitors per day, or visitors by source.

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Five Ways to Run Analytics on MongoDB – Their Pros and Cons

Rockset

Developers choose this database because of its flexible data model and its inherent scalability as a NoSQL database. Yet, analytics is now a vital part of modern data applications. The benefit of these tools is that they’re built specifically for data analytics. The downsides of data warehouses are data and query latency.

MongoDB 52
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ELT Explained: What You Need to Know

Ascend.io

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

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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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Real-Time Analytics on DynamoDB - Using DynamoDB Streams with Lambda and ElastiCache

Rockset

Low data latency requirements rule out ETL-based solutions which increase your data latency above the real-time threshold and inevitably lead to “ETL hell”. DynamoDB is a fully managed NoSQL database provided by AWS that is optimized for point lookups and small range scans using a partition key.

NoSQL 40
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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

For data scientists, these skills are extremely helpful when it comes to manage and build more optimized data transformation processes, helping models achieve better speed and relability when set in production. Examples of NoSQL databases include MongoDB or Cassandra.

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Top Big Data Hadoop Projects for Practice with Source Code

ProjectPro

There are various kinds of hadoop projects that professionals can choose to work on which can be around data collection and aggregation, data processing, data transformation or visualization. How small file problems in streaming can be resolved using a NoSQL database. Using Flume to handle small files in streaming.

Hadoop 40