Based on our record, Apache Flink should be more popular than Databricks. It has been mentiond 27 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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
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
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
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
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
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 21 days ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.