keep_column_case When writing a table from Spark to Snowflake, the Spark connector defaults to shifting the letters in column names to uppercase, unless the column names are in double quotes. An operation is a method, which can be applied on a RDD to accomplish certain task. The command gives warning, creates directory in dfs but not the table in hive metastore. Because Parquet data files use a block size of 1 GB by default, an INSERT might fail (even for a very small amount of data) if your HDFS is running low on space. Operations in PySpark DataFrame are lazy in nature but, in case of pandas we get the result as soon as we apply any operation. 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. 2 I am looking for help in trying to resolve an issue where writing to parquet files is getting increasingly slower. Introduction to Big Data and PySpark Upskill data scientists in the Big Data technologies landscape and Pyspark as a distributed processing engine LEVEL: BEGINNER DURATION: 2-DAYS COURSE DELIVERED: AT YOUR OFFICE What you will learn This two-days course will provide a hands-on introduction to the Big Data ecosystem, Hadoop and Apache Spark in. Write and Read Parquet Files in Spark/Scala. As I already explained in my previous blog posts, Spark SQL Module provides DataFrames (and DataSets - but Python doesn't support DataSets because it's a dynamically typed language) to work with structured data. A Spark DataFrame or dplyr operation. Alpakka Documentation. This article is about how to use a Glue Crawler in conjunction with Matillion ETL for Amazon Redshift to access Parquet files. By default, Spark’s scheduler runs jobs in FIFO fashion. The following are code examples for showing how to use pyspark. If we are using earlier Spark versions, we have to use HiveContext which is. mergeSchema is false (to avoid schema merges during writes which. write I’ve found that spending time writing code in PySpark has. Custom language backend can select which type of form creation it wants to use. In this blog post, I'll share example #3 and #4 from my presentation to demonstrate capabilities of Spark SQL Module. In this talk we will explore the concepts and motivations behind continuous applications and how Structured Streaming Python APIs in Apache Spark 2. language agnostic, open source Columnar file format for analytics. Contributing my two cents, I’ll also answer this. In order to work with PySpark, start a Windows Command Prompt and change into your SPARK_HOME directory. in the Parquet. Write a Pandas dataframe to Parquet format on AWS S3. The beauty is you don't have to change a single line of code after the Context initialization, because pysparkling's API is (almost) exactly the same as PySpark's. Source is an internal distributed store that is built on hdfs while the. A python job will then be submitted to a Apache Spark instance running on AWS EMR, which will run a SQLCon. The finalize action is executed on the S3 Parquet Event Handler. The runtime will usually correlate directly with the language you selected to write your function. S3 V2 connector documentation mentions i t can be used with data formats such as Avro, Parquet etc. See the complete profile on LinkedIn and discover Vagdevi’s. Hortonworks. Args: switch (str, pyspark. Spark runs on Hadoop, Mesos, standalone, or in the cloud. A recent example is the new version of our retention report that we recently released, which utilized Spark to crunch several data streams (> 1TB a day) with ETL (mainly data cleansing) and analytics (a stepping stone towards full click-fraud detection) to produce the report. The job eventually fails. 0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. Reference What is parquet format? Go the following project site to understand more about parquet. EMR Glue Catalog Python Spark Pyspark Step Example - emr_glue_spark_step. saveAsTable deprecated in Spark 2. Please note that it is not possible to write Parquet to Blob Storage using PySpark. useIPython as false in interpreter setting. Write and Read Parquet Files in Spark/Scala. A python job will then be submitted to a Apache Spark instance running on AWS EMR, which will run a SQLContext to create a temporary table using a DataFrame. View Rajendra Reddy Pallala’s profile on LinkedIn, the world's largest professional community. The lineage diagram for the above source code is generated using Python Spark Lineage and it is displayed below:. destination_df. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). Parquet is a special case here: its committer does no extra work other than add the option to read all newly-created files then write a schema summary. 2 hrs to transform 8 TB of data without any problems successfully to S3. 文件在hdfs上,该文件每行都有一些中文字符,用take()函数查看,发现中文不会显示,全是显示一些其他编码的字符,但是各个地方,该设置的编码的地方,我都设置了utf-8编码格式,但不知道为何显示不出 论坛. You can also set the compression codec as uncompressed , snappy , or lzo. If you specify multiple rules in a replication configuration, Amazon S3 prioritizes the rules to prevent conflicts when filtering. In this page, I am going to demonstrate how to write and read parquet files in HDFS. From the memory store the data is flushed to S3 in parquet format, sorted by key (figure 7). Apache Spark with Amazon S3 Python Examples. Below is pyspark code to convert csv to parquet. Hi, I have an 8 hour job (spark 2. Contributed Recipes¶. We plan to use Spark SQL to query this file in a distributed. Dataframes in Pyspark can be created in multiple ways: Data can be loaded in through a CSV, JSON, XML or a Parquet file. DataFrame Parquet support. Spark; SPARK-18402; spark: SAXParseException while writing from json to parquet on s3. Parquet file: If you compress your file and convert it to Apache Parquet, you end up with 1 TB of data in S3. Developing custom Machine Learning (ML) algorithms in PySpark—the Python API for Apache Spark—can be challenging and laborious. 5 and Spark 1. Alpakka Documentation. Cassandra + PySpark DataFrames revisted. In this blog post, I’ll share example #3 and #4 from my presentation to demonstrate capabilities of Spark SQL Module. First, let me share some basic concepts about this open source project. This source is used whenever you need to write to Amazon S3 in Parquet format. I'm trying to read in some json, infer a schema, and write it out again as parquet to s3 (s3a). See the complete profile on LinkedIn and. Assisted in post 2013 flood damage proposal writing. Spark is a big, expensive cannon that we data engineers wield to destroy anything in our paths. PYSPARK QUESTIONS 1 PYSPARK QUESTIONS 3 Download all the data for these questions from this LINK QUESTION 2 For each department calculate the total items, maximum and. This notebook will walk you through the process of building and using a time-series analysis model to forecast future sales from historical sales data. The runtime will usually correlate directly with the language you selected to write your function. on_left + expr. Use: parquet-tools To look at parquet data and schema off Hadoop filesystems systems. Moreover you still need to get Jupyter notebook running with PySpark, which is again not too difficult, but also out of scope for a starting point. key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a. Pyspark get json object. Write to Parquet on S3 ¶ Create the inputdata:. com | Documentation | Support | Community. Apache Spark with Amazon S3 Python Examples. S3 V2 connector documentation mentions i t can be used with data formats such as Avro, Parquet etc. Hi All, I need to build a pipeline that copies the data between 2 system. saveAsTable method using pyspark. Speeding up PySpark with Apache Arrow ∞ Published 26 Jul 2017 By. Specifies which Amazon S3 objects to replicate and where to store the replicas. context import GlueContext from awsglue. PySpark supports custom profilers, this is to allow for different profilers to be used as well as outputting to different formats than what is provided in the BasicProfiler. Using the PySpark module along with AWS Glue, you can create jobs that work with data over. Apache Spark with Amazon S3 Python Examples. Before applying transformations and actions on RDD, we need to first open the PySpark shell (please refer to my previous article to setup PySpark). Supported file formats and compression codecs in Azure Data Factory. Parquet : Writing data to s3 slowly. destination_df. Read/Write Output Using Local File System and Amazon S3 in Spark First step to process any data in spark is to read it and be able to write it. You can also. When creating schemas for the data on S3 the positional order is important. I'm getting an Exception when I try to save a DataFrame with a DeciamlType as an parquet file. To switch execution of a script from PySpark to pysparkling, have the code initialize a pysparkling Context instead of a SparkContext, and use the pysparkling Context to set up your RDDs. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). PySpark ETL. Alternatively we can use the key and secret from other locations, or environment variables that we provide to the S3 instance. More than 1 year has passed since last update. While the first two options can be used when accessing S3 from a cluster running in your own data center. transforms import * from awsglue. Summary: Spark (and Pyspark) use map, mapValues, reduce, reduceByKey, aggregateByKey, and join to transform, aggregate, and connect datasets. They are extracted from open source Python projects. PYSPARK QUESTIONS 9 PYSPARK QUESTIONS 10 DOWNLOAD ALL THE DATA FOR THESE QUESTIONS FROM THIS LINK QUESTIONS 10 Find the customer first name , last name , day of the week of shopping, street name remove double quotes and street number and customer state. Choosing an HDFS data storage format- Avro vs. You can vote up the examples you like or vote down the exmaples you don't like. This mistake ended up costing more than a thousand dollars and didn’t make my advisor happy. {SparkConf, SparkContext}. sql import SparkSession • >>> spark = SparkSession\. StringType(). Similar performance gains have been written for BigSQL, Hive, and Impala using Parquet storage, and this blog will show you how to write a simple Scala application to convert existing text-base data files or tables to Parquet data files, and show you the actual storage savings and query performance boost for Spark SQL. StructType(). Let's look at two simple scenarios I would like to do. The Spark integration has explicit handling for Parquet to enable it to support the new committers, removing this (slow on S3) option. PySpark Dataframe Sources. foreach() in Python to write to DynamoDB. Apache Parquet offers significant benefits to any team working with data. Pyspark - Read JSON and write Parquet If you were able to read Json file and write it to a Parquet file successfully then you should have a parquet folder created in your destination directory. Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working at Allstate Insurance, for the guest post below about his experiences comparing use of the Apache Avro and Apache Parquet file formats with Apache Spark. If I run the above job in scala everything works as expected (without having to adjust the memoryOverhead). It is built on top of Akka Streams, and has been designed from the ground up to understand streaming natively and provide a DSL for reactive and stream-oriented programming, with built-in support for backpressure. 2 PySpark … (Py)Spark 15. s3a://mybucket/work/out. memoryOverhead to 3000 which delays the errors but eventually I get them before the end of the job. Reference What is parquet format? Go the following project site to understand more about parquet. AWS Glue is an ETL service from Amazon that allows you to easily prepare and load your data for storage and analytics. PythonForDataScienceCheatSheet PySpark -SQL Basics InitializingSparkSession SparkSQLisApacheSpark'smodulefor workingwithstructureddata. I have a huge amount of data that I cannot load in one go. 1) and pandas (0. You can pass the. S3 Parquetifier. At the time of this writing, there are three different S3 options. OGG BigData Replicat Writing to AWS S3 Errors With "Caused by: java. saveAsTable deprecated in Spark 2. It’s becoming more common to face situations where the amount of data is simply too big to handle on a single machine. 3 Vectorized Pandas UDFs: Lessons Intro to PySpark Workshop 2018-01-24 – Garren's [Big] Data Blog on Scaling Python for Data Science using Spark Spark File Format Showdown – CSV vs JSON vs Parquet – Garren's [Big] Data Blog on Tips for using Apache Parquet with Spark 2. If we are using earlier Spark versions, we have to use HiveContext which is. The Bleeding Edge: Spark, Parquet and S3. 1> RDD Creation a) From existing collection using parallelize meth. Use: parquet-tools To look at parquet data and schema off Hadoop filesystems systems. It can access diverse data sources including HDFS, Cassandra, HBase, and S3. context import GlueContext from awsglue. For some reason, about a third of the way through the. I was testing writing DataFrame to partitioned Parquet files. The following code snippet shows you how to read elasticsearch index from python. withStorageConfig (HoodieStorageConfig) limitFileSize (size = 120MB) Property: hoodie. By default, Spark’s scheduler runs jobs in FIFO fashion. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. However, I would like to find a way to have the data in csv/readable. An operation is a method, which can be applied on a RDD to accomplish certain task. transforms import RenameField from awsglue. PyArrow - Python package to interoperate Arrow with Python allowing to convert text files format to parquet files among other functions. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Parquet : Writing data to s3 slowly. I have some. Transformations, like select() or filter() create a new DataFrame from an existing one. Thus far the only method I have found is using Spark with the pyspark. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. Column :DataFrame中的列 pyspark. The following are code examples for showing how to use pyspark. Sending Parquet files to S3. There are a lot of things I'd change about PySpark if I could. In order to work with PySpark, start a Windows Command Prompt and change into your SPARK_HOME directory. Read Dremel made simple with Parquet for a good introduction to the format while the Parquet project has an in-depth description of the format including motivations and diagrams. Parquet file in Spark Basically, it is the columnar information illustration. The Bleeding Edge: Spark, Parquet and S3. The latest Tweets from Apache Parquet (@ApacheParquet). following codes show you how to read and write from local file system or amazon S3 / process the data and write it into filesystem and S3. For Introduction to Spark you can refer to Spark documentation. Other actions like ` save ` write the DataFrame to distributed storage (like S3 or HDFS). Apache Zeppelin dynamically creates input forms. Spark SQL is a Spark module for structured data processing. You can edit the names and types of columns as per your. It is that the best choice for storing long run massive information for analytics functions. DataFrame: 将分布式数据集分组到指定列名的数据框中 pyspark. on_left + expr. In this article we will learn to convert CSV files to parquet format and then retrieve them back. DataFrame创建一个DataFrame。 当schema是列名列表时,将从数据中推断出每个列的类型。. 2 0 100 200 300 400 500 600 700 TimeinSeconds Wide - hive-1. 1) Last updated on JUNE 05, 2019. This page shows how to operate with Hive in Spark including: Create DataFrame from existing Hive table Save DataFrame to a new Hive table Append data. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). You can also set the compression codec as uncompressed , snappy , or lzo. Column): column to "switch" on; its values are going to be compared against defined cases. Pyspark can read the original gziped text files, query those text files with SQL, apply any filters, functions, i. Anyone got any ideas, or are we stuck with creating a Parquet managed table to access the data in Pyspark?. 5 and Spark 1. The command is quite straight forward and the data set is really a sample from larger data set in Parquet; the job is done in PySpark on YARN and written to HDFS:. Hi, We have a large binary file, that we want to be able to search (do a range query on key). regression import. Transformations, like select() or filter() create a new DataFrame from an existing one. Halfway through my application, I get thrown with a org. I don't see df. However, due to timelines pressure, it may be hard to pivot, and in those cases S3 could be leveraged to store the application state and configuration files. I hope you guys got an idea of what PySpark DataFrame is, why is it used in the industry and its features in this PySpark DataFrame tutorial. For general information and examples of Spark working with data in different file formats, see Accessing External Storage from Spark. The underlying implementation for writing data as Parquet requires a subclass of parquet. The following are code examples for showing how to use pyspark. 2 PySpark … (Py)Spark 15. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. View Vagdevi Barlanka’s profile on LinkedIn, the world's largest professional community. types import * from pyspark. If you specify multiple rules in a replication configuration, Amazon S3 prioritizes the rules to prevent conflicts when filtering. Copy the first n files in a directory to a specified destination directory:. parquet function to create the file. path: The path to the file. GitHub Gist: instantly share code, notes, and snippets. I have been using PySpark recently to quickly munge data. So "Parquet files on S3" actually seems to satisfy most of our requirements: Its columnar format makes adding new columns to existing data not excruciatingly painful Files are compressed by the encoding scheme resulting in hilariously small Parquet files compared to the same data as a CSV file. Choosing an HDFS data storage format- Avro vs. Summary: Spark (and Pyspark) use map, mapValues, reduce, reduceByKey, aggregateByKey, and join to transform, aggregate, and connect datasets. The parquet file destination is a local folder. First, let me share some basic concepts about this open source project. Spark SQL is a Spark module for structured data processing. Other file sources include JSON, sequence files, and object files, which I won't cover, though. 2 PySpark … (Py)Spark 15. 3, but we've recently upgraded to CDH 5. Jump to page: Pyarrow table. 0, Parquet readers used push-down filters to further reduce disk IO. Document licensed under the Creative Commons Attribution ShareAlike 4. CSV took 1. job import Job from awsglue. The S3 Event Handler is called to load the generated Parquet file to S3. I have a huge amount of data that I cannot load in one go. SQL queries will then be possible against the temporary table. This method assumes the Parquet data is sorted by time. Select the Permissions section and three options are provided (Add more permissions, Edit bucket policy and Edit CORS configuration). How do you know that it's writing CSV format instead of Parquet format in Snowflake? The reason I am asking is that, when you use the Snowflake Spark connector, the data is stored in a table in a Snowflake database, in a compressed format, not directly to a s3 files. PySpark Cheat Sheet: Spark in Python This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning. Supported file formats and compression codecs in Azure Data Factory. If we are using earlier Spark versions, we have to use HiveContext which is. A python job will then be submitted to a local Apache Spark instance which will run a SQLContext to create a temporary table and load the Parquet file contents into a DataFrame. There is around 8 TB of data and I need to compress it. Users sometimes share interesting ways of using the Jupyter Docker Stacks. PySpark Dataframe Sources. Coarse-Grained Operations: These operations are applied to all elements in data sets through maps or filter or group by operation. DataFrame: 将分布式数据集分组到指定列名的数据框中 pyspark. 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. If I run the above job in scala everything works as expected (without having to adjust the memoryOverhead). Read and Write DataFrame from Database using PySpark. My program reads in a parquet file that contains server log data about requests made to our website. Transformations, like select() or filter() create a new DataFrame from an existing one. Required options are kafka. A compliant, flexible and speedy interface to Parquet format files for Python. It can also take in data from HDFS or the local file system. The parquet file destination is a local folder. PySpark (Py)Spark / Spark PyData Spark Spark Hadoop PyData PySpark 13. This time I am going to try to explain how can we use Apache Arrow in conjunction with Apache Spark and Python. For some reason, about a third of the way through the. The underlying implementation for writing data as Parquet requires a subclass of parquet. PySpark: Creating RDD->DF->Parquet with a schema that has 1000's of fields but rows with variable number of columns apache-spark hadoop elasticsearch pyspark parquet Updated March 12, 2019 07:26 AM. The PySparking is a pure-Python implementation of the PySpark RDD interface. Hi, I am using localstack s3 in unit tests for code where pyspark reads and writes parquet to s3. Apache Parquet offers significant benefits to any team working with data. ArcGIS Enterprise Functionality Matrix ArcGIS Enterprise is the foundational system for GIS, mapping and visualization, analytics, and Esri’s suite of applications. Zeppelin and Spark: Merge Multiple CSVs into Parquet Introduction The purpose of this article is to demonstrate how to load multiple CSV files on an HDFS filesystem into a single Dataframe and write to Parquet. This article is about how to use a Glue Crawler in conjunction with Matillion ETL for Amazon Redshift to access Parquet files. Using PySpark, the following script allows access to the AWS S3 bucket/directory used to exchange data between Spark and Snowflake. Contributed Recipes¶. The Bleeding Edge: Spark, Parquet and S3. size Target size for parquet files produced by Hudi write phases. Add any additional transformation logic. Provide the File Name property to which data has to be written from Amazon S3. The example reads the emp. But if there is no know issues with doing spark in a for loop I will look into other possibilities for memory leaks. Steps given here is applicable to all the versions of Ubunut including desktop and server operating systems. dynamicframe import DynamicFrame, DynamicFrameReader, DynamicFrameWriter, DynamicFrameCollection from pyspark. RecordConsumer. For some reason, about a third of the way through the. Vagdevi has 1 job listed on their profile. Apache Spark and Amazon S3 — Gotchas and best practices. Source is an internal distributed store that is built on hdfs while the. I tried to run below cpyspark code to read /write parquet files in redshift database from S3. We call this a continuous application. I was testing writing DataFrame to partitioned Parquet files. 9 and the Spark Livy REST server. The following are code examples for showing how to use pyspark. transforms import * from awsglue. context import SparkContext. filterPushdown option is true and spark. You can also. PySpark RDD API DataFrame API RDD Resilient Distributed Dataset = Spark Java DataFrame RDD / R data. In this article we will learn to convert CSV files to parquet format and then retrieve them back. A compliant, flexible and speedy interface to Parquet format files for Python. Operations in PySpark DataFrame are lazy in nature but, in case of pandas we get the result as soon as we apply any operation. What gives? Works with master='local', but fails with my cluster is specified. Required options are kafka. Vagdevi has 1 job listed on their profile. Amazon EMR. Parquet writes getting very slow when using partitionBy. ) cluster I try to perform write to S3 (e. on_left + expr. AWS Glue is an ETL service from Amazon that allows you to easily prepare and load your data for storage and analytics. StackShare helps you stay on top of the developer tools and services that matter most to you. But when I write (parquet)the df out to S3, the files are indeed placed in S3 in the correct location, but 3 of the 7 columns are suddenly missing data. Parquet file in Spark Basically, it is the columnar information illustration. Services publish JSON events into a RabbitMQ queue, this is the only concern we think the guys writing the services should have. This repository demonstrates some of the mechanics necessary to load a sample Parquet formatted file from an AWS S3 Bucket. A Spark DataFrame or dplyr operation. Worker node PySpark Executer JVM Driver JVM Executer JVM Executer JVM Storage Python VM Worker node Worker node Python VM Python VM RDD API PySpark Worker node Executer JVM Driver JVM Executer JVM Executer JVM Storage Python VM. Row: DataFrame数据的行 pyspark. Spark SQL 3 Improved multi-version support in 1. 0 documentation. Thanks for the compilation fix! Too bad that the project on GitHub does not include issues where this could be mentioned, because it is quite a useful fix. textFile("/path/to/dir"), where it returns an rdd of string or use sc. Optimized Write to S3\n", "\n", "Finally, we physically partition the output data in Amazon S3 into Hive-style partitions by *pick-up year* and *month* and convert the data into Parquet format. You can vote up the examples you like or vote down the exmaples you don't like. CSV to Parquet. They are extracted from open source Python projects. 4), pyarrow (0. I'm getting an Exception when I try to save a DataFrame with a DeciamlType as an parquet file. It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. I tried to increase the spark. In a web-browser, sign in to the AWS console and select the S3 section. 0 and earlier, Spark jobs that write Parquet to Amazon S3 use a Hadoop commit algorithm called FileOutputCommitter by default. From the memory store the data is flushed to S3 in parquet format, sorted by key (figure 7). mergeSchema is false (to avoid schema merges during writes which. If I run the above job in scala everything works as expected (without having to adjust the memoryOverhead). It is that the best choice for storing long run massive information for analytics functions. S3 guarantees that a file is visible only when the output stream is properly closed. Column :DataFrame中的列 pyspark. Vagdevi has 1 job listed on their profile. You can potentially write to a local pipe and have something else reformat and write to S3. An operation is a method, which can be applied on a RDD to accomplish certain task. PYSPARK QUESTIONS 9 PYSPARK QUESTIONS 10 DOWNLOAD ALL THE DATA FOR THESE QUESTIONS FROM THIS LINK QUESTIONS 10 Find the customer first name , last name , day of the week of shopping, street name remove double quotes and street number and customer state. Quick Reference to read and write in different file format in Spark Write. Before applying transformations and actions on RDD, we need to first open the PySpark shell (please refer to my previous article to setup PySpark). Hi, I am using localstack s3 in unit tests for code where pyspark reads and writes parquet to s3. Would appreciate if some one loo. Clone via HTTPS Clone with Git or checkout with SVN using the repository's web address. A typical Spark workflow is to read data from an S3 bucket or another source, perform some transformations, and write the processed data back to another S3 bucket. foreach() in Python to write to DynamoDB. Operations in PySpark DataFrame are lazy in nature but, in case of pandas we get the result as soon as we apply any operation. Cassandra + PySpark DataFrames revisted. Summary: Spark (and Pyspark) use map, mapValues, reduce, reduceByKey, aggregateByKey, and join to transform, aggregate, and connect datasets. Using Parquet format has two advantages. DataFrame we write it out to a parquet storage. Getting an RDD back and transforming it to a DataFrame requires doing a query in the JVM, serializing about a gazallion objects to send to the Python virtual machine over the Java Gateway server, deserialize with Py4J, then reencode the entire thing and send back to the JVM. I'm getting an Exception when I try to save a DataFrame with a DeciamlType as an parquet file. Sample code import org.