Spark Read Parquet From S3


Files will be in binary format so you will not able to read them. spark read hive table (3). Check out this post for example of how to process JSON data from Kafka using Spark Streaming. Also, can read from distributed file systems , local file systems, cloud storage (S3), and external relational database systems through JDBC. Batch processing is typically performed by reading data from HDFS. Read and Write DataFrame from Database using PySpark. Parquet (or ORC) files from Spark. Minimize Read and Write Operations for Parquet. Although AWS S3 Select has support for Parquet, Spark integration with S3 Select for Parquet didn't give speedups similar to the CSV/JSON sources. compression. Athena is an AWS serverless database offering that can be used to query data stored in S3 using SQL syntax. Recently we were working on a problem where the parquet compressed file had lots of nested tables and some of the tables had columns with array type and our objective was to read it and save it to CSV. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. Contribute to jeanycyang/spark-mongodb-parquet-s3 development by creating an account on GitHub. Run the job again. All I am getting is "Failed to read Parquet file. Find the Parquet files and rewrite them with the correct schema. parquet ( path ). Parquet is a columnar format, supported by many data processing systems. With the relevant libraries on the classpath and Spark configured with valid credentials, objects can be can be read or written by using their URLs as the path to data. Write / Read Parquet File in Spark. To use Parquet with Hive 0. I suspect there could be a lot of performance found if more engineering time were put into the Parquet reader code for Presto. In this first blog post in the series on Big Data at Databricks, we explore how we use Structured Streaming in Apache Spark 2. Parquet (or ORC) files from Spark. The basic setup is to read all row groups and then read all groups recursively. In this scenario, you create a Spark Batch Job using tS3Configuration and the Parquet components to write data on S3 and then read the data from S3. …including a vectorized Java reader, and full type equivalence. The example below shows how to read a Petastorm dataset as a Spark RDD object:. This increases speed, decreases storage costs, and provides a shared format that both Dask dataframes and Spark dataframes can understand, improving the ability to use both computational systems in the same workflow. This scenario applies only to subscription-based Talend products with Big Data. I have seen a few projects using Spark to get the file schema. Presently, MinIO’s implementation of S3 Select and Apache Spark supports JSON, CSV and Parquet file formats for query pushdowns. Parquet & Spark. dataframe users can now happily read and write to Parquet files. Spark Read Parquet From S3. It can be very easy to use Spark to convert XML to Parquet and then query and analyse the output data. X • Contributions by 75+ orgs, ~250 individuals • Distributed algorithms that scale linearly with the data 7. R is able to see the files in S3, we can read directly from S3 and copied them to the local environment, but we can't make Spark read them when using sparklyr. So, therefore, you have to reduce the amount of data to fit your computer memory capacity. Let’s see an example of using spark-select with spark-shell. We are experimenting with display styles that make it easier to read articles in PMC. Job scheduling and dependency management is done using Airflow. We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. In this article I will talk about one of the experiment I did couple of months ago to understand how Parquet predicate filter pushdown works with EMR/Spark SQL. Question by BigDataRocks Feb 02, 2017 at 05:59 PM Spark spark-sql sparksql amazon Just wondering if spark supports Reading *. Code Read aws configuration. key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a:// protocol also set the values for spark. Spark uses libraries from Hadoop to connect to S3, and the integration between Spark, Hadoop, and the AWS services is very much a work in progress. That is, every day, we will append partitions to the existing Parquet file. Parquet file in Spark Basically, it is the columnar information illustration. It also reads the credentials from the "~/. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. from_pandas(). ParquetInputFormat. For example, in handling the between clause in query 97:. The easiest way to get a schema from the parquet file is to use the 'ParquetFileReader' command. Read a Parquet file into a Spark DataFrame. The ePub format uses eBook readers, which have several "ease of reading" features already built in. You can use Blob Storage to expose data publicly to the world, or to store application data privately. This scenario applies only to a subscription-based Talend solution with Big data. Copy, paste and run the following code: val data. ORC format was introduced in Hive version 0. See screenshots, read the latest customer reviews, and compare ratings for Apache Parquet Viewer. 1 pre-built using Hadoop 2. Can anyone explain what I need to do to fix this?. For optimal performance when reading files saved in the Parquet format, read and write operations must be minimized, including generation of summary metadata, and coalescing metadata from multiple files. sparklyr, developed by RStudio, is an R interface to Spark that allows users to use Spark as the backend for dplyr, which is the popular data manipulation package for R. As in, if you test read you have to do something with the data after or Spark will say "all done" and skip the read. —Matei Zaharia, VP, Apache Spark, Founder & CTO, Databricks ” Spark Core Engine Spark SQL Spark Streaming. Native Parquet Support Hive 0. Optimized Row Columnar (ORC) file format is a highly efficient columnar format to store Hive data with more than 1,000 columns and improve performance. Users can save a Pandas data frame to Parquet and read a Parquet file to in-memory Arrow. The reason to write this blog is to share more advanced information on this topic that I could not find anywhere and had to learn myself. If 'auto', then the option io. Users can mix SQL queries with Spark programs and seamlessly integrates with other constructs of Spark. Files will be in binary format so you will not able to read them. To use Parquet with Hive 0. In order to quickly generate value for the business and avoid the complexities of a Spark/Hadoop based project, Sisense's CTO Guy Boyangu opted for a solution based on Upsolver, S3 and Amazon Athena. Source is an internal distributed store that is built on hdfs while the. The ePub format uses eBook readers, which have several "ease of reading" features already built in. Parquet (or ORC) files from Spark. Configure AWS credentials for Spark (conf/spark-defaults. If I am using MapReduce Parquet Java libraries and not Spark SQL, I am able to read it. S3, on the other hand, has always been touted as one of the best ( reliable, available & cheap ) object storage available to mankind. Spark-Snowflake Integration with Full Query Pushdown: Spark using the Snowflake connector with the new pushdown feature enabled. If you want to use a csv file as source, before running startSpark. Datasets in parquet format can be read natively by Spark, either using Spark SQL or by reading data directly from S3. S3, on the other hand, has always been touted as one of the best ( reliable, available & cheap ) object storage available to mankind. Spark Read Parquet From S3. Data produced by production jobs go into the Data Lake, while output from ad-hoc jobs go into Analysis Outputs. You can also add it as Maven dependency, sbt-spark-package or a jar import. Push-down filters allow early data selection decisions to be made before data is even read into Spark. 12 you must download the Parquet Hive package from the Parquet project. Get S3 Data. Run the job again. Pandas is a good example of using both projects. Use Spark to read Cassandra data efficiently as a time series; Partition the Spark dataset as a time series; Save the dataset to S3 as Parquet; Analyze the data in AWS; For your reference, we used Cassandra 3. I also had to ingest JSON data from an API endpoint. That's it! You now have a Parquet file, which is a single file in our case, since the dataset is really small. Download files. Working with Parquet. Job scheduling and dependency management is done using Airflow. Writing a Parquet. Spark SQL can automatically infer the schema of a JSON dataset, and use it to load data into a DataFrame object. One of the projects we're currently running in my group (Amdocs' Technology Research) is an evaluation the current state of different option for reporting on top of and near Hadoop (I hope I'll be able to publish the results when. Check out this post for example of how to process JSON data from Kafka using Spark Streaming. We will run through the following steps: creating a simple batch job that reads data from Cassandra and writes the result in parquet in S3. Most jobs run once a day, processing data from. To learn about Azure Data Factory, read the S3 in Parquet or. It ensures fast execution of existing Hive queries. If ‘auto’, then the option io. parquet() function. Tests are run on a Spark cluster with 3 c4. A tutorial on how to use the open source big data platform, Alluxio, as a means of creating faster storage access and data sharing for Spark jobs. If you going to be processing the results with Spark, then parquet is a good format to use for saving data frames. Parquet metadata caching is a feature that enables Drill to read a single metadata cache file instead of retrieving metadata from multiple Parquet files during the query-planning phase. We will run through the following steps: creating a simple batch job that reads data from Cassandra and writes the result in parquet in S3. First argument is sparkcontext that we are. The process for converting to columnar formats using an EMR cluster is as follows: Create an EMR cluster with Hive installed. sparklyr, developed by RStudio, is an R interface to Spark that allows users to use Spark as the backend for dplyr, which is the popular data manipulation package for R. 0 Arrives! Apache Spark 2. Parquet metadata caching is a feature that enables Drill to read a single metadata cache file instead of retrieving metadata from multiple Parquet files during the query-planning phase. We've written a more detailed case study about this architecture, which you can read here. Pandas can directly work on top of Arrow columns, paving the way for a faster Spark integration. Parquet (or ORC) files from Spark. We will use Hive on an EMR cluster to convert and persist that data back to S3. For optimal performance when reading files saved in the Parquet format, read and write operations must be minimized, including generation of summary metadata, and coalescing metadata from multiple files. ParquetInputFormat. Select a Spark application and type the path to your Spark script and your arguments. 0; use_dictionary (bool or list) – Specify if we should use dictionary encoding in general or only for some columns. Parquet schema allows data files "self-explanatory" to the Spark SQL applications through the Data Frame APIs. This blog post will cover how I took a billion+ records containing six years of taxi ride metadata in New York City and analysed them using Spark SQL on Amazon EMR. Before using the Parquet Output step, you will need to select and configure the shim for your distribution, even if your Location is set to 'Local'. If you are reading from a secure S3 bucket be sure that the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables are both defined. However, because Parquet is columnar, Redshift Spectrum can read only the column that is relevant for the query being run. Pandas is a good example of using both projects. gz files from an s3 bucket or dir as a Dataframe or Dataset. Select a Spark application and type the path to your Spark script and your arguments. Run the job again. Categories. Reliably utilizing Spark, S3 and Parquet: Everybody says 'I love you'; not sure they know what that entails October 29, 2017 October 30, 2017 ywilkof 5 Comments Posts over posts have been written about the wonders of Spark and Parquet. The Parquet Output step requires the shim classes to read the correct data. If 'auto', then the option io. Apache Spark and Parquet (SParquet) are a match made in scalable data analytics and delivery heaven. 6 with Spark 2. To use Parquet with Hive 0. Source is an internal distributed store that is built on hdfs while the. Users can save a Pandas data frame to Parquet and read a Parquet file to in-memory Arrow. textFile() method. Working with Parquet. Parquet schema allows data files "self-explanatory" to the Spark SQL applications through the Data Frame APIs. conf): spark. Find the Parquet files and rewrite them with the correct schema. This recipe either reads or writes a S3 dataset. Because of consistency model of S3, when writing: Parquet (or ORC) files from Spark. This is the documentation of the Python API of Apache Arrow. I uploaded the script in an S3 bucket to make it immediately available to the EMR platform. The basic setup is to read all row groups and then read all groups recursively. Let’s now try to read some data from Amazon S3 using the Spark SQL Context. conf spark. A tutorial on how to use the open source big data platform, Alluxio, as a means of creating faster storage access and data sharing for Spark jobs. 11 and Spark 2. Create table query for the Flow logs stored in S3 bucket as Snappy compressed Parquet files. 4 • Part of the core distribution since 1. Instead, you should used a distributed file system such as S3 or HDFS. Athena uses Amazon S3 as its underlying data store, making your data highly available and durable. This topic explains how to access AWS S3 buckets by mounting buckets using DBFS or directly using APIs. With Apache Spark you can easily read semi-structured files like JSON, CSV using standard library and XML files with spark-xml package. Building A Data Pipeline Using Apache Spark. 0 (April 2015) • Runs SQL / HiveQL queries, optionally alongside or replacing existing Hive deployments. parquet 파일로 로컬 컴퓨터에 저장을 시키고 나아가 S3 버킷에 저장을 시킨다. Data will be stored to a temporary destination: then renamed when the job is successful. This recipe either reads or writes a S3 dataset. By integrating the loading mechanism with the Query engine (Catalyst optimizer) it is often possible to push down filters and projections all the way to the data source minimizing data transfer. It was a matter of creating a regular table, map it to the CSV data and finally move the data from the regular table to the Parquet table using the Insert Overwrite syntax. Use these tips to troubleshoot errors. 11 to use and retain the type information from the table definition. Spark on S3 with Parquet Source (Snappy): Spark reading from S3 directly with data files formatted as Parquet and compressed with Snappy. gz files from an s3 bucket or dir as a Dataframe or Dataset. With Spark 2. Parquet is a columnar format, supported by many data processing systems. This guide will give you a quick introduction to working with Parquet files at Mozilla. Arguments; If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. Installation. It ensures fast execution of existing Hive queries. Contribute to jeanycyang/spark-mongodb-parquet-s3 development by creating an account on GitHub. Try to read the Parquet dataset with schema merging enabled: spark. The default io. To evaluate this approach in isolation, we will read from S3 using S3A protocol,. Note that the Spark job script needs to be submitted to the master node (and will then be copied on the slave nodes by the Spark platform). The basic setup is to read all row groups and then read all groups recursively. gz files from an s3 bucket or dir as a Dataframe or Dataset. 6 with Spark 2. I am curious, for using Impala to query parquet files from S3, does it seek only download the needed columns, or it download the whole file first? I remember S3 files being an object so that it doesnt allow to seek specific bytes which is needed to efficiently use parquet files. Athena is an AWS serverless database offering that can be used to query data stored in S3 using SQL syntax. Short example of on how to write and read parquet files in Spark. io/s3/cli/aws/python/boto3/2018/09/10/AWS-CLI-And-S3. Remaining section would concentrate on reading and writing data between Spark and various data sources. Apache Spark and Parquet (SParquet) are a match made in scalable data analytics and delivery heaven. " It is the same when it is uncompressed or zipped. Reading and Writing the Apache Parquet Format¶. All I am getting is "Failed to read Parquet file. This topic explains how to access AWS S3 buckets by mounting buckets using DBFS or directly using APIs. Contribute to jeanycyang/spark-mongodb-parquet-s3 development by creating an account on GitHub. Amazon S3 is a service for storing large amounts of unstructured object data, such as text or binary data. 4; File on S3 was created from Third Party - See Reference Section below for specifics on how the file was created. This article shows a sample code to load data into Hbase or MapRDB(M7) using Scala on Spark. You can also refer to Spark's documentation on the subject here. Bigstream Hyper-acceleration can provide a performance boost to almost any Spark application due to our platform approach to high performance Big Data and Machine Learning. 4; I am able to process my data and create the correct dataframe in pyspark. Configuring my first Spark job. Labels: aws, spark, glue, parquet No comments. is a columnar file format that provides optimizations to speed up queries and is a far more efficient file format than CSV or JSON. Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language. With the relevant libraries on the classpath and Spark configured with valid credentials, objects can be can be read or written by using their URLs as the path to data. Categories. 0") – The Parquet format version, defaults to 1. By integrating the loading mechanism with the Query engine (Catalyst optimizer) it is often possible to push down filters and projections all the way to the data source minimizing data transfer. Analyzing Java Garbage Collection Logs for debugging and optimizing Apache Spark jobs 10 minute read Recently while trying to make peace between Apache Parquet, Apache Spark and Amazon S3, to write data from Spark jobs, we were running into recurring issues. Parquet & Spark. The Parquet Output step requires the shim classes to read the correct data. Parquet is a columnar format that is supported by many other data processing systems. Now, given that we already know we have, or can create, CSV representations of data sets, the sequence of steps to get to "Parquet on S3" should be clear: Download and read a CSV file into a Pandas DataFrame; Convert the DataFrame into an pyarrow. Handles nested parquet compressed content. Users can save a Pandas data frame to Parquet and read a Parquet file to in-memory Arrow. Arguments; If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. It also reads the credentials from the "~/. Spark SQL is a Spark module for structured data processing. Most of our derived datasets, like the longitudinal or main_summary tables, are stored in Parquet files. Like JSON datasets, parquet files. Use Spark to read Cassandra data efficiently as a time series; Partition the Spark dataset as a time series; Save the dataset to S3 as Parquet; Analyze the data in AWS; For your reference, we used Cassandra 3. Contribute to jeanycyang/spark-mongodb-parquet-s3 development by creating an account on GitHub. Let's convert to Parquet! Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. Aditya Verma 7,009 views. I have seen a few projects using Spark to get the file schema. In the step section of the cluster create statement, specify a script stored in Amazon S3, which points to your input data and creates output data in the columnar format in an Amazon S3 location. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. It also reads the credentials from the "~/. There is also a small amount of overhead with the first spark. Read and Write Data To and From Amazon S3 Buckets in Rstudio. parquet ( path ). Java Write Parquet File. In this scenario, you create a Spark Batch Job using tS3Configuration and the Parquet components to write data on S3 and then read the data from S3. Configuring my first Spark job. parquet 파일이 생성된 것을 확인한다. Editor’s Note: Since this post was written in 2015, The HDF Group has developed HDF5 Connector for Apache Spark™, a new product that addresses the challenges of adapting large scale array-based computing to the cloud and object storage while intelligently handling the full data management life cycle. Most of our derived datasets, like the longitudinal or main_summary tables, are stored in Parquet files. Bigstream Solutions. In Memory In Server Big Data Small to modest data Interactive or batch work Might have many thousands of jobs Excel, R, SAS, Stata,. The latter is commonly found in hive/Spark usage. 6 times faster than reading directly from S3. Code is run in a spark-shell. engine is used. - While fetching all the columns for a single now using a condition like "where origin = 'LNY' and AirTime = 16;", ORC has an edge over Parquet because the ORC format has a light index along with each file. Any suggestions on this issue?. This blog post will demonstrate that it's easy to follow the AWS Athena tuning tips with a tiny bit of Spark code - let's dive in!. 12 you must download the Parquet Hive package from the Parquet project. Spark codebase and support materials around it. Normally we use Spark for preparing data and very basic analytic tasks. The main goal is to make it easier to build end-to-end streaming applications, which integrate with storage, serving systems, and batch jobs in a consistent and fault-tolerant way. Similar to reading data with Spark, it’s not recommended to write data to local storage when using PySpark. Spark SQL, DataFrames and Datasets Guide. Working with Amazon S3, DataFrames and Spark SQL. This blog post will demonstrate that it's easy to follow the AWS Athena tuning tips with a tiny bit of Spark code - let's dive in!. It is that the best choice for storing long run massive information for analytics functions. 회사 내에 Amazon emr cluster서버가 있고, 현재 데이터 백업용으로 s3를 쓴다. I also had to ingest JSON data from an API endpoint. Take the pain out of XML processing on Spark. Job scheduling and dependency management is done using Airflow. Spark-Snowflake Integration with Full Query Pushdown: Spark using the Snowflake connector with the new pushdown feature enabled. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. This query would only cost $1. I was able to read the parquet file in a sparkR session by using read. engine behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. One of the projects we're currently running in my group (Amdocs' Technology Research) is an evaluation the current state of different option for reporting on top of and near Hadoop (I hope I'll be able to publish the results when. For information on configuring a shim for a specific distribution, see Set Up Pentaho to Connect to a Hadoop Cluster. To evaluate this approach in isolation, we will read from S3 using S3A protocol,. 1 pre-built using Hadoop 2. Today we explore the various approaches one could take to improve performance while writing a Spark job to read and write parquet data to & from S3. Reading and Writing Data Sources From and To Amazon S3. Now, we can use a nice feature of Parquet files which is that you can add partitions to an existing Parquet file without having to rewrite existing partitions. I solved the problem by dropping any Null columns before writing the Parquet files. text("people. But in Spark 1. For example, you might want to create daily snapshots of a database by reading the entire contents of a table, writing to this sink, and then other programs can analyze the contents of the specified file. Due to various differences in how Pig and Hive map their data types to Parquet, you must select a writing Flavor when DSS writes a Parquet dataset. The incremental conversion of your JSON data set to Parquet will be a little bit more annoying to write in Scala than the above example, but is very much doable. Apache Spark and S3 Select can be integrated via spark-shell, pyspark, spark-submit etc. ParquetInputFormat. Parquet metadata caching is available for Parquet data in Drill 1. Normally we use Spark for preparing data and very basic analytic tasks. txt") A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. This makes parsing JSON files significantly easier than before. We will run through the following steps: creating a simple batch job that reads data from Cassandra and writes the result in parquet in S3. Let's convert to Parquet! Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. Active How to read parquet data from S3 to spark dataframe Python? 0. Data in all domains is getting bigger. In the step section of the cluster create statement, specify a script stored in Amazon S3, which points to your input data and creates output data in the columnar format in an Amazon S3 location. >>> df4 = spark. acceleration of both reading and writing using numba. When writing a DataFrame as Parquet, Spark will store the frame's schema as metadata at the root of the directory. Spark: Reading and Writing to Parquet Format ----- - Using Spark Data Frame save capability - Code/Approach works on both local HDD and in HDFS environments Related video: Introduction to Apache. We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. This reduces significantly input data needed for your Spark SQL applications. One of its earliest and most used services is Simple Storage Service or simply S3. Spark Read Parquet From S3. Native Parquet Support Hive 0. parquet ( path ). Reliably utilizing Spark, S3 and Parquet: Everybody says 'I love you'; not sure they know what that entails October 29, 2017 October 30, 2017 ywilkof 5 Comments Posts over posts have been written about the wonders of Spark and Parquet. The advantages of having a columnar storage are as follows − Spark SQL provides support for both reading and writing parquet files that automatically capture the schema of the original data. spark read hive table (3). Part 1 but more recently into cloud storage like Amazon S3. Broadly speaking, there are 2 APIs for interacting with Spark: DataFrames/SQL/Datasets: general, higher level API for users of Spark; RDD: a lower level API for spark internals and advanced programming. Parquet is not "natively" supported in Spark, instead, Spark relies on Hadoop support for the parquet format - this is not a problem in itself, but for us it caused major performance issues when we tried to use Spark and Parquet with S3 - more on that in the next section; Parquet, Spark & S3. We want to read data from S3 with Spark. One of the projects we're currently running in my group (Amdocs' Technology Research) is an evaluation the current state of different option for reporting on top of and near Hadoop (I hope I'll be able to publish the results when. " It is the same when it is uncompressed or zipped. We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. 2 and trying to append a data frame to partitioned Parquet directory in S3. Installation. Apache Drill will create multiples files for the tables, depending on the size and configuration your environment. Table via Table. Reliably utilizing Spark, S3 and Parquet: Everybody says 'I love you'; not sure they know what that entails October 29, 2017 October 30, 2017 ywilkof 5 Comments Posts over posts have been written about the wonders of Spark and Parquet. Configure AWS credentials for Spark (conf/spark-defaults. Parquet file in Spark Basically, it is the columnar information illustration. Any suggestions on this issue?. JavaBeans and Scala case classes representing. Batch processing is typically performed by reading data from HDFS. One such change is migrating Amazon Athena schemas to AWS Glue schemas. Parquet (or ORC) files from Spark. This scenario applies only to subscription-based Talend products with Big Data. I will introduce 2 ways, one is normal load us How to build and use parquet-tools to read parquet files. 1, both straight open source versions. Parquet library to use. 8 in the AMPLab in 2014 • Migration to Spark DataFrames started with Spark 1. " It is the same when it is uncompressed or zipped. ec2의 이슈 때문에 데이터가 날라가서 데이터를 s3에서 가져와서 다시 내 몽고디비 서버에 넣어야 했다. Question by BigDataRocks Feb 02, 2017 at 05:59 PM Spark spark-sql sparksql amazon Just wondering if spark supports Reading *. I suspect there could be a lot of performance found if more engineering time were put into the Parquet reader code for Presto. The Data Lake. This blog post will cover how I took a billion+ records containing six years of taxi ride metadata in New York City and analysed them using Spark SQL on Amazon EMR. This source is used whenever you need to write to Amazon S3 in Parquet format. conf): spark. Page is the unit of read within a parquet file. What is even more strange , when using "Parquet to Spark" I can read this file from the proper target destination (defined in the "Spark to Parquet" node) but as I mentioned I cannot see this file by using "S3 File Picker" node or "aws s3 ls" command. Data will be stored to a temporary destination: then renamed when the job is successful.