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The latest release of Precog AI-powered Data Loader has enhanced support for Tableau and Tableau Data Prep, allowing end users unprecedented access to 1000s more data sources.
JSON and NoSQL datasets come in many forms. Some datasets are essentially tabular. Others are complex multidimensional structures.
Precog, the company behind the popular Precog solution for transforming and loading complex data for analytics and data science, has partnered with SME Solutions Group, Inc. based in Tampa, Florida.
Precog opens up the C3.ai Covid Data Lake to millions or new users that don’t have the technical expertise to work directly with the API documentation and Python tools provided by C3.
Wouldn’t it be great if everyone was empowered to access and analyse this data with no code or workflows?
What is AI for Data Integration? It’s really about understanding the data, the relationships between the data regardless of structure, and above all else it’s about maintaining the meaning of the analytic row regardless of how complex the data is at the start
The number of web APIs has grown exponentially over the past decade. Today, nearly every product in every sector exposes a public API, and organizations commonly deploy private APIs to share data internally.
When using Snowflake without Precog, the data must first be downloaded from the source. This immediately makes stale data more likely and introduces risks associated with incorrect or outdated results.
Tables in Precog are streaming and virtualised. We can think of this as though the Precog table were a grocery list rather than the groceries themselves. We can use the same list in different stores the same way we can use the same Precog table with different datasets. We can also export tables and import them into different copies of Precog.
Using Precog we can push the tabulated data into SQL databases and warehouses such as Snowflake and Postgres.
Precog shows us that in the adverse events dataset, drug characterizations are denoted as numbers. These numbers represent whether or not the drug was suspected as causing the adverse reaction or interacting with a suspected drug.
Now that the data has been tabulated, we can analyse it using software such as Microsoft Power BI.
This article follows in Keshav’s footsteps, showing how to derive insights from JSON healthcare data using Snowflake and Precog.
With Precog, we connect directly to the source of the data. In this case the source is the FDA.gov Web API. This ensures that we are always working with the latest data including new records and corrections.
Using Precog is as easy as “connect – pick – load – analyze” your data as shown below. No coding, complicated scripts, clunky SSH clients or anything else technical to learn or use.
Engineered to transform variable, complex JSON data, Precog draws source data from file output, API URLs, and data repositories such as AWS S3 and Azure Blob Storage.
The Precog solution enables data analysts and engineers to access complex JSON data as tables. In many cases we want those tables to be stored in Microsoft SQL Server or some other SQL database engine. Such requirement can be implemented easily using Precog and Azure Data Factory.
Whether you’re a professional data scientist or studying to become a data scientist, you’ll likely need to work with a JSON dataset. JSON isn’t easy to work with. It’s not tabular, and you can’t just push it to a SQL database — at least not without a “bit” of work.
There are over 200,000 API?s on the web today, and more being added all the time. And the vast majority of these API?s provide JSON data via a REST interface. Unless you are a developer you generally can?t do much with this data or get any value from it. API?s are by design built for developers.
The Multidimensional Relational Algebra, or MRA, is at the core of everything we do at Precog. It’s what enables our product to provide such a smooth and intuitive user experience.
You’ve probably been hearing about “big data” for a while now ? the term has been around for years, and in the meantime, data has only gotten bigger. A whole industry has sprung up around it, from collection to storage to analytics, and data integration engineers are part of that puzzle. But what if your company doesn’t have one?
The ETL (extract, transform, load) industry has been around for decades. Its primary purpose is to move data from source locations to data warehouses so analytics and data science teams can access the data in one place and perform analysis across a range of critical data sources.
Are you trying to work with huge complex data sets in Alteryx Designer? Would you like to? Would you like to increase processing performance of Designer by 100x for many data sets? Would you like to be able to connect to live data sources like API’s or MongoDB?? Then the Precog Macro for Alteryx Designer can help.