Databricks VS Amazon Redshift

Databricks Reviews

Amazon Redshift Reviews

Databricks mentions (17)

• dolly-v2-12b

  Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: about 1 year ago

• Clickstream data analysis with Databricks and Redpanda

  Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / over 1 year ago

• DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It

  Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / almost 2 years ago

• A Quick Start to Databricks on AWS

  Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 2 years ago

• data science workspace/notebook solution thoughts?

  I am considering Hex, Deepnote, and possibly Databricks. Does anyone have any experience using the first 2 (i have worked with Databricks in the past) and have thoughts they can share? The company isn't doing any fancy data science so far so I mostly want it for deep product analytics which I can turn into reports that are easily shareable across the org. That being said, I do want to get into statistical... Source: about 2 years ago

Amazon Redshift mentions (26)

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  They differ from conventional analytic databases like Snowflake, Redshift, BigQuery, and Oracle in several ways. Conventional databases are batch-oriented, loading data in defined windows like hourly, daily, weekly, and so on. While loading data, conventional databases lock the tables, making the newly loaded data unavailable until the batch load is fully completed. Streaming databases continuously receive new... - Source: dev.to / 2 months ago

• Choosing the Right AWS Database: A Guide for Modern Applications

  Data warehousing is the process of storing and analyzing large volumes of data for business intelligence and analytics purposes. AWS offers a fully managed data warehousing service called Amazon Redshift that can handle petabyte-scale data warehouses with ease. - Source: dev.to / 6 months ago

• A Comprehensive Guide to AWS DynamoDB vs. Redshift for Databases and Data Warehouses

  The topics of databases and data warehouses are central to the modern data landscape, and Amazon's offeringsDynamoDB and Redshiftare standout products in their respective categories. Here's a detailed comparison:. - Source: dev.to / 8 months ago

• AWS Redshift: Robust and Scalable Data Warehousing

  Amazon Redshift is a powerful, scalable data warehousing service within the AWS ecosystem. It excels in handling large datasets with its columnar storage, parallel query execution, and features like Redshift Spectrum and RA3 instances. Redshift’s clustered architecture, robust security, and integration with AWS services make it a go-to choice for businesses needing efficient and secure data management solutions. - Source: dev.to / 11 months ago

• AWS Beginner's Key Terminologies

  Amazon Redshift (analytics) Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. With Amazon Redshift, you can analyze your data using your existing business intelligence tools. Https://aws.amazon.com/redshift/. - Source: dev.to / over 1 year ago