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[DOC] Presto SQL > Trino rebranding: update URLs (prestosql.io > trino.io) [IG-18308] #348

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Sharon-iguazio merged 1 commit into from
Mar 21, 2021
Merged

[DOC] Presto SQL > Trino rebranding: update URLs (prestosql.io > trino.io) [IG-18308] #348

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2 changes: 1 addition & 1 deletion data-ingestion-and-preparation/README.ipynb
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"source": [
"### Running Full ANSI Presto SQL Queries\n",
"\n",
"The platform has a default pre-deployed Presto service that enables using the [Presto](https://prestosql.io/) open-source distributed SQL query engine to run interactive SQL queries and perform high-performance low-latency interactive analytics on data that's stored in the platform.\n",
"The platform has a default pre-deployed Presto service that enables using the [Presto](https://trino.io/) open-source distributed SQL query engine to run interactive SQL queries and perform high-performance low-latency interactive analytics on data that's stored in the platform.\n",
"To run a Presto query from a Jupyter notebook, all you need is to use an SQL magic command — `%sql` followed by your Presto query.\n",
"Such queries are executed as distributed queries across the platform's application nodes.\n",
"The [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparationipynb) tutorial demonstrates how to run Presto queries using SQL magic.\n",
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2 changes: 1 addition & 1 deletion data-ingestion-and-preparation/README.md
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Expand Up @@ -219,7 +219,7 @@ You can run SQL queries on NoSQL and Parquet data in the platform's data store,

### Running Full ANSI Presto SQL Queries

The platform has a default pre-deployed Presto service that enables using the [Presto](https://prestosql.io/) open-source distributed SQL query engine to run interactive SQL queries and perform high-performance low-latency interactive analytics on data that's stored in the platform.
The platform has a default pre-deployed Presto service that enables using the [Presto](https://trino.io/) open-source distributed SQL query engine to run interactive SQL queries and perform high-performance low-latency interactive analytics on data that's stored in the platform.
To run a Presto query from a Jupyter notebook, all you need is to use an SQL magic command — `%sql` followed by your Presto query.
Such queries are executed as distributed queries across the platform's application nodes.
The [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparationipynb) tutorial demonstrates how to run Presto queries using SQL magic.
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2 changes: 1 addition & 1 deletion data-ingestion-and-preparation/basic-data-ingestion-and-preparation.ipynb
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"You can run SQL statements (`SELECT` only) on top of NoSQL tables in the platform's data store.\n",
"To do this, you need to use the Jupyter `%sql` or `%%sql` IPython Jupyter magic followed by an SQL statement.\n",
"The platform supports standard ANSI SQL semantics.\n",
"Under the hood, the SQL statements are executed via [Presto](https://prestosql.io/), which is a distributed SQL engine designed from the ground up for fast analytics queries.\n",
"Under the hood, the SQL statements are executed via [Presto](https://trino.io/), which is a distributed SQL engine designed from the ground up for fast analytics queries.\n",
"\n",
"In the example in the following cell, as a preparation for the SQL query, the **stocks.csv** file that was ingested to the **users/<running user>/examples/stocks** platform data-container directory in the previous [Ingesting Files from Amazon S3 to the Platform](#ingest-from-amazon-s3) example is written to a **stocks_example_tab** NoSQL table in the same directory.\n",
"Then, an SQL `SELECT` query is run on this table.\n",
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2 changes: 1 addition & 1 deletion platform-overview.ipynb
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"\n",
"- [Apache Spark](https://spark.apache.org/) data-processing engine — including the Spark SQL and Datasets, MLlib, R, and GraphX libraries — with real-time access to the platform's NoSQL data store and file system.\n",
" See the platform's [Spark API references](https://www.iguazio.com/docs/v3.0/data-layer/reference/spark-apis/) and the examples in the [**spark-sql-analytics**](data-ingestion-and-preparation/spark-sql-analytics.ipynb) tutorial.\n",
"- [Presto](https://prestosql.io/) distributed SQL query engine, which can be used to run interactive SQL queries over platform NoSQL tables or other object (file) data sources.\n",
"- [Presto](https://trino.io/) distributed SQL query engine, which can be used to run interactive SQL queries over platform NoSQL tables or other object (file) data sources.\n",
" See the platform's [Presto documentation](https://www.iguazio.com/docs/v3.0/data-layer/presto/).\n",
"- [pandas](https://pandas.pydata.org/) Python analysis library, including structured DataFrames.\n",
"- [Dask](https://dask.org/) parallel-computation Python library, including scaled pandas DataFrames.\n",
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