Yes, RATH allows connecting to a MySQL database. RATH supports database types including MySQL, ClickHouse, Amazon Athena, Amazon Redshift, Apache Spark SQL, Apache Doris, Apache Hive, Apache Impala, Apache Kylin, Oracle, and PostgreSQL.
For more details about currently supported databases, refer to the Supported databases section.
RATH is adding more database support such as Snowflake, etc.
RATH natively supports uploading CSV and JSON files for data visualization. As soon as you log in to the RATH portal, click on the File button on the left. You can upload a CSV or JSON file from your local environment with customized encoding.
Different from conventional BI software such as PowerBI and Tableau, the concepts of data source and data engine are utterly separated in RATH.
Considering the scenario that you want to import data from a Clickhouse service. In this case, ClickHouse is the data source, while RATH functions as the data engine.
On the other hand, you can import data from other data sources, and set Clickhouse as the data engine. So when there are large amounts of data, RATH forwards the queries to Clickhouse distributed clusters for faster processing.
Best practice: use RATH to perform a quick analysis of your dataset. After getting a better understanding of your datasets, adjust the data types, and run your data with RATH again to generate a more accurate result. Repeat until you get a satisfactory result.
Currently, the community version of RATH caps data at 100MB. However, you can easily work with a much larger data source by sampling.
Sampling is the process to select a reasonable size of a subset from your original dataset. For example, you don’t have to knock on the doors of every resident in a large city to survey their opinion on a candidate, instead, you call a sample of, let’s say 1000 people.
For advanced users, you can also connect RATH to an MPP database, or subscribe to RATH Pro.
You can perform automated exploratory data analysis with the Mega-auto exploration feature in RATH. Refer to our tutorials chapter for a step-by-step guide.
It is generally not recommended to use Mega-auto Exploration feature for large datasets since the free online version of RATH does not use distributed data engines. For large datasets, use Semi-auto Exploration instead.
RATH does have Restful API support but is not ready for being public yet. For inquiries, contact RATH support for more details.
Tensor search is a method for searching multiple data sources at the same time, using tensors (multi-dimensional arrays) and linear algebra techniques. It is often used in information retrieval and natural language processing and can be more efficient than traditional search algorithms.
Imagine you have a database of customer reviews for different products and you want to search for reviews that mention a particular product feature. You could represent each review as a tensor, with one dimension for the text of the review and another for the product being reviewed. You could then represent the search query, which is the product feature you are interested in, as a tensor. Using techniques from linear algebra, you could compare the search query tensor to the review tensors and rank the results based on their similarity. This would allow you to quickly find all of the reviews that mention the desired product feature, rather than having to search each review individually.
RATH hasn't adopted Tensor search capacities yet.