This is an excerpt from the Scala Cookbook (partially modified for the internet). HIVE vs Parquet. Note: Spark accepts JSON data in the new-line delimited JSON Lines format, which basically means the JSON file must meet the below 3 requirements, Each Line of the file is a JSON Record ; Line Separator must be '\n' or '\r\n' Data must be UTF-8. For this type of intermediate data, we generally use the Avro file format. You can use exactly the same code to read multiple JSON files. name print " -- File to process: %s" % acme_file Read the CSV from S3 into Spark dataframe. You can insert JSON data in SnappyData tables and execute queries on the tables. So if you run Spark Streaming program with fileStream and not create any new files, you may not see any output on screen. json function. Its very easy to read a JSON file and construct Spark dataframes. 0 and above. They are extracted from open source Python projects. json") JSON file above should have one json object per line. Go through the complete video and learn how to work on nested JSON using spark and parsing the nested JSON files in integration and become a data scientist by enrolling the course. json')) I would like the file to contain a list of d. Spark is a quintessential part of the Apache data stack: built atop of Hadoop, Spark is intended to handle resource-intensive jobs such as data streaming and graph processing. Simply open your Python files in your HDInsight workspace and connect to Azure. What is PySpark? Apache Spark is an open-source cluster-computing framework which is easy and speedy to use. Dataframe in Spark is another features added starting from version 1. Type: Improvement Status: Open. Reading a json file into a RDD (not dataFrame) using pyspark. how to read multi-line json in spark. spark:spark-streaming-kafka-0-8_2. I can use the following code to read a single json file but I need to read multiple json files and merge them into one Dataframe. using the read. I'll guess that many people reading this have spend time wrestling with configuration to get Python and Spark to play nicely. In single-line mode, a file can be split into many parts and read in parallel. 04 - Embedded & Distributed Apache Drill - Query File System, JSON, and Parquet Apache Drill - HBase query Apache Drill - Hive query Apache Drill - MongoDB query. Remember that the main advantage to using Spark DataFrames vs those other programs is that Spark can handle data across many RDDs, huge data sets that would never fit on a single computer. The default for spark csv is to write output into partitions. The spark session read table will create a data frame from the whole table that was stored in a disk. Each event become a record in the Avro file, where the Body contains the original JSON string that was sent as UTF-8 bytes. We come across various circumstances where we receive data in json format and we need to send or store it in csv format. zip and pyspark. According to Apache, Py4J enables Python programs running in a Python interpreter to dynamically access Java objects in a JVM. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. pyspark --packages com. The pyspark API provides an interface for working with Spark, and in this blog post we’d like to show you how easy it is to get started with pyspark and begin taking advantage of all it has to offer. Using S3 Select with Spark to Improve Query Performance. 1 employs Spark SQL's built-in functions to allow you to consume data from many sources and formats (JSON, Parquet, NoSQL), and easily perform transformations and interchange between these data formats (structured, semi-structured, and unstructured data). io Find an R package R language docs Run R in your browser R Notebooks. wholeTextFiles). Welcome to the HadoopExam PySpark Structured Streaming Professional Training with HandsOn Sessions. Dataframes in Pyspark can be created in multiple ways: Data can be loaded in through a CSV, JSON, XML or a Parquet file. txt") A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. You can insert JSON data in SnappyData tables and execute queries on the tables. 2 Streaming Apache Drill with ZooKeeper install on Ubuntu 16. json() function, which loads data from a directory of JSON files where each line of the files is a JSON object. Then, a file with the name _SUCCESStells whether the operation was a success or not. This interactivity brings the best properties of Python and Spark to developers and empowers you to gain faster insights. Apache Spark is a fast and general-purpose cluster computing system. In this tutorial, we shall look into examples addressing different scenarios of reading multiple text files to single RDD. using the read. Read text file in PySpark - How to read a text file in PySpark? The PySpark is very powerful API which provides functionality to read files into RDD and perform various operations. This Spark SQL tutorial with JSON has two parts. In fact, it even automatically infers the JSON schema for you. Configure PySpark driver to use Jupyter Notebook: running pyspark will automatically open a Jupyter Notebook. zip") Can someone tell me how to get the contents of A. Fast Data Analytics with Spark and Python (PySpark) District Data Labs 2. A certain set of operations must be performed on each DF ( treating each as a single partition), and some results must be returned from each processing. In this blog post, we introduce Spark SQL's JSON support, a feature we have been working on at Databricks to make it dramatically easier to query and create JSON data in Spark. 1BestCsharp blog 4,143,794 views. Plotly's Python library is free and open source! Get started by downloading the client and reading the primer. An R interface to Spark. Pyspark: Split multiple array columns into rows - Wikitechy get specific row from spark dataframe By clicking "Sign up" you indicate that you have read and. hadoopConfiguration) conf. 5, with more than 100 built-in functions introduced in Spark 1. Any equivalent from within the databricks platform?. A SparkSession can be used to create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. We will show examples of JSON as input source to Spark SQL’s SQLContext. And, in this example, I'd like to show you … how to read a json file. The Docker image I was using was running Spark 1. {SparkConf, SparkContext}. …r orc file in DataFrameReader. py — and we can also add a list of dependent files that will be located together with our main file during execution. This page serves as a cheat sheet for PySpark. Java Project Tutorial - Make Login and Register Form Step by Step Using NetBeans And MySQL Database - Duration: 3:43:32. jdbc - Connecting from Spark/pyspark to PostgreSQL Mysql query relating to count and date time - android - AppCompat Snackbar not centered on table html - Get current session info using separate lin MySQL PHP not storing full file to BLOB - Update trigger on postgresql - visual studio 2013 - Can not uninstall VS2013 CE -. Then setup the SPARK_HOME env variable. In this article, you use Jupyter Notebook available with HDInsight Spark clusters to run a job that reads data from a Data Lake Storage account. Load JSON data in spark data frame and read it; Store into hive non-partition table; Components Involved. First, the files may not be readable (for instance, they could be missing, inaccessible or corrupted). pyspark-csv An external PySpark module that works like R's read. CSV to Parquet. In this configuration, spark master is yarn, mesos or standalone. databricks:spark-avro_2. Databricks provides a unified interface for handling bad records and files without interrupting Spark jobs. 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. Edureka's PySpark Certification Training is designed to provide you the knowledge and skills that are required to become a successful Spark Developer using Python. sql into multiple files. PySpark SQL is a higher-level abstraction module over the PySpark Core. I'll guess that many people reading this have spend time wrestling with configuration to get Python and Spark to play nicely. PySpark code is converted into Scala code before execution. You can do this by starting pyspark with. Read a table serialized in the JavaScript Object Notation format into a Spark DataFrame. In this blog post, we introduce Spark SQL's JSON support, a feature we have been working on at Databricks to make it dramatically easier to query and create JSON data in Spark. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. As an example, we will look at Durham police crime reports from the Dhrahm Open Data website. Please follow this medium post on how to. Spark has moved to a dataframe API since version 2. In this blog post, I'll walk you through how to use an Apache Spark package from the community to read any XML file into a DataFrame. 04 - Embedded & Distributed Apache Drill - Query File System, JSON, and Parquet Apache Drill - HBase query Apache Drill - Hive query Apache Drill - MongoDB query. py --arg1 val1. how to read multi-line json in spark. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs. Reading a json file into a RDD (not dataFrame) using pyspark. This page serves as a cheat sheet for PySpark. Needing to read and write JSON data is a common big data task. path: location of files. With Amazon EMR release version 5. Apache Spark has as its architectural foundation the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. Apache Spark 1. When several consecutive recipes in a DSS Flow (including with branches or splits) use the Spark engine, DSS can automatically merge all of these recipes and run them as a single Spark job, called a Spark pipeline. However, there is often a need to run manipulate hdfs file directly from python. You can process a fie at a time. Spark SQL JSON Overview. PySpark code is converted into Scala code before execution. {"a": 1, "b": 2} 이것은 파이썬 json. 이제,이 파일을 parkpark를 사용하여 Spark의 DataFrame으로 읽으려고합니다. Unfortunately there’s no “data import” tool, or a command line client for now. 4 Method to convert json to parquet File format: The following method needs is using the JavaSparkContext, SparkSession object to create session and read the schema and convert the data to parquet format. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. json("example. Spark’s default file format is Parquet. Read into RDD Spark Context The first thing a Spark program requires is a context, which interfaces with some kind of cluster to use. sql and we want to import SparkSession … and then we want to create a spark context … which is the variable again that gives us a reference point. json reader, which other than files, can also read from RDD. Reading & Writing to text files. In that case, a spark job would be separated in multiple tasks and each node would be dedicated to different tasks. DataFrames in Pyspark can be created in multiple ways: JSON, XML, or a Parquet file. First, let’s go over how submitting a job to PySpark works: spark-submit --py-files pyfile. If you are just playing around with DataFrames you can use show method to print DataFrame to console. Converting a nested JSON document to CSV using Scala, Hadoop, and Apache Spark Posted on Feb 13, 2017 at 6:48 pm Usually when I want to convert a JSON file to a CSV I will write a simple script in PHP. Underlying processing of dataframes is done by RDD's , Below are the most used ways to create the dataframe. Note that the file that is offered as a json file is not a typical JSON file. Reading JSON Nested Array in Spark DataFrames In a previous post on JSON data, I showed how to read nested JSON arrays with Spark DataFrames. 0 and above. textFile = sc. gl/vnZ2kv This video has not been monetized and does not. @seahboonsiew / No release yet / (1). This can only be used to assign a new storage level if the RDD does not have a storage level set yet. This is Recipe 12. This blog explains and demonstrates through explicit examples how data engineers, data scientists, and data analysts collaborate and combine their efforts to construct complex data pipelines using Notebook Workflows on Databricks’ Unified Analytics Platform. Go through the complete video and learn how to work on nested JSON using spark and parsing the nested JSON files in integration and become a data scientist by enrolling the course. Part 2 covers a “gotcha” or something you might not expect when using Spark SQL JSON data source. Developers. Let's now try to read some data from Amazon S3 using the Spark SQL Context. The following are code examples for showing how to use pyspark. Converts parquet file to json using spark. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. Once the data is loaded, however, figuring out how to access individual fields is not so straightforward. Spark - Read JSON file to RDD JSON has become one of the most common data format that is being exchanged between nodes in internet and applications. Spark SQL. 5 alone; so, we thought it is a good time for revisiting the subject, this time also utilizing the external package spark-csv, provided by Databricks. Each event become a record in the Avro file, where the Body contains the original JSON string that was sent as UTF-8 bytes. I am reading a csv file with pyspark to extract some information from it. I can't use read. Data is processed in Python and cached and shuffled in the JVM. In addition to a name and the function itself, the return type can be optionally specified. Spark SQL executes upto 100x times faster than Hadoop. Load a JSON file which comes with Apache Spark distributions by default. Write and Read Parquet Files in Spark/Scala. This makes parsing JSON files significantly easier than before. the schema inference inside PySpark (and maybe Scala Spark as. The requirement is to load JSON Data into Hive Partitioned table using Spark. In this tutorial, you will learn about the various file formats in Spark and how to work on them. SPARK-24149: Retrieve all federated namespaces tokens. DataFrames have built in operations that allow you to query your data, apply filters, change the schema, and more. zip") Can someone tell me how to get the contents of A. When using PySpark to load multiple JSON files from S3 I get an error and the Spark job fails if a file is missing. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. gl/vnZ2kv This video has not been monetized and does not. parallelize(fileList). Pyspark: multiple conditions in when clause - Wikitechy get specific row from spark dataframe By clicking "Sign up" you indicate that you have read and agree. A folder /out_employees/ is created with a JSON file and status if SUCCESS or FAILURE. Spark SQL allows to read data from folders and tables by Spark session read property. json reader, which other than files, can also read from RDD. csv files inside all the zip files using pyspark. When you read in a layer, ArcGIS Enterprise layers must be converted to Spark DataFrames to be used by geoanalytics or pyspark functions. take(1), it returns the first line instead of the first file. PYSPARK: PySpark is the python binding for the Spark Platform and API and not much different from the Java/Scala versions. Learn how to use Spark & Hive Tools for Visual Studio Code to create and submit PySpark scripts for Apache Spark, first we'll describe how to install the Spark & Hive tools in Visual Studio Code and then we'll walk through how to submit jobs to Spark. Now that I am more familiar with the API, I can describe an easier way to access such data, using the explode() function. From what I have read, the "--conf " argument to spark-submit should do basically the same thing so I am not sure if setting the variable would work even if I knew how to set it through CDH Manager. Loading and Saving Data in Spark. Download file Aand B from here. Data Engineers Will Hate You - One Weird Trick to Fix Your Pyspark Schemas May 22 nd , 2016 9:39 pm I will share with you a snippet that took out a lot of misery from my dealing with pyspark dataframes. Read and Write files on HDFS In the above examples, we have read and written the file on the local file system. Priority: Major. Edureka's PySpark Certification Training is designed to provide you the knowledge and skills that are required to become a successful Spark Developer using Python. Simply put them under the lib/ directory for the workflow. You can vote up the examples you like or vote down the ones you don't like. We need to import the necessary pySpark modules for Spark, Spark Streaming, and Spark Streaming with Kafka. The spark session read table will create a data frame from the whole table that was stored in a disk. Converting a nested JSON document to CSV using Scala, Hadoop, and Apache Spark Posted on Feb 13, 2017 at 6:48 pm Usually when I want to convert a JSON file to a CSV I will write a simple script in PHP. 创建dataframe 2. csv files inside all the zip files using pyspark. What Spark adds to existing frameworks like Hadoop are the ability to add multiple map and reduce tasks to a single workflow. 이제,이 파일을 parkpark를 사용하여 Spark의 DataFrame으로 읽으려고합니다. To achieve the requirement, below components will be used: Hive - It is used to store data in a non-partitioned table with ORC file format. If you have any query related to Spark and Hadoop, kindly refer our Big Data Hadoop & Spark Community. select() the best way to read subsets of columns in spark from a parquet file? Are there any other options. 문서화에 이어, 나는 이것을하고있다. environ['PYSPARK_SUBMIT_ARGS'] = '--packages org. In this article we are trying to join a Flat File with a JSON file by using SPARK SQL. JSON to DataFrame. textFile("hdfs:///data/*. In that case, a spark job would be separated in multiple tasks and each node would be dedicated to different tasks. With Amazon EMR release version 5. read-json-files - Databricks. json function. Following is the JSON file we will try to. 3 for Hive Metastore Client 2. Let's load the Spark shell and see an example:. Read a table serialized in the JavaScript Object Notation format into a Spark DataFrame. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. According to Apache, Py4J enables Python programs running in a Python interpreter to dynamically access Java objects in a JVM. @seahboonsiew / No release yet / (1). path: location of files. select() the best way to read subsets of columns in spark from a parquet file? Are there any other options. spark read json (4) 이 JSON 파일이 있습니다. For Introduction to Spark you can refer to Spark documentation. Writing from PySpark to MySQL Database Hello, I am trying to learn PySpark and have written a simple script that loads some JSON files from one of my HDFS directories, loads each in as a python dictionary (using json. With Apache Spark you can easily read semi-structured files like JSON, CSV using standard library and XML files with spark-xml package. sql import SparkSession from optimus import Optimus spark = SparkSession. We will show examples of JSON as input source to Spark SQL’s SQLContext. Create a Spark DataFrame: Read and Parse Multiple (Small) Files We take a look at how to work with data sets without using UTF -16 encoded files in Apache Spark using the Scala language. If i do this in plain python (that is without pyspark), i can do this with the following and it works!!!. Therefore, let's break the task into sub-tasks: Load the text file into Hive table. Apache Spark has as its architectural foundation the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. class pyspark. Below is pyspark code to convert csv to parquet. 04 - Embedded & Distributed Apache Drill - Query File System, JSON, and Parquet Apache Drill - HBase query Apache Drill - Hive query Apache Drill - MongoDB query. from pyspark. column_name. Convert JSON to CSV. Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. June 9, JSON File. Longtime python user, but Pyspark noob. Second, even if the files are processable, some records may not be parsable (for example, due to syntax errors and schema mismatch). Spark supports the efficient parallel application of map and reduce operations by dividing data up into multiple partitions. json("example. See the Cloud Dataproc Quickstarts for instructions on creating a cluster. I am trying to parse json data in Pyspark's map function. Spark SQL JSON with Python Example Tutorial Part 1. Dataframe in Spark is another features added starting from version 1. You can vote up the examples you like or vote down the ones you don't like. If i do this in plain python (that is without pyspark), i can do this with the following and it works!!!. Our plan is to extract data from snowflake to Spark using SQL and pyspark. With Apache Spark you can easily read semi-structured files like JSON, CSV using standard library and XML files with spark-xml package. Actually here the vectors are not native SQL types so there will be performance overhead one way or another. This is a quick step by step tutorial on how to read JSON files from S3. Processing 450 small log files took 42. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Included are a set of APIs that that enable MapR users to write applications that consume MapR Database JSON tables and use them in Spark. Posted on July 11, 2017 by jinglucxo how to read multi-li… on spark read sequence file(csv o. We will discuss on how to work with AVRO and Parquet files in Spark. The Spark MapR. This post will walk through reading top-level fields as well as JSON arrays and nested. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. Fast Data Analytics with Spark and Python 1. We come across various circumstances where we receive data in json format and we need to send or store it in csv format. A dataframe in Spark is similar to a SQL table, an R dataframe, or a pandas dataframe. In the following example, we do just that and then print out the data we got:. Once the data is loaded, however, figuring out how to access individual fields is not so straightforward. we will use | for or, & for and , ! for not. From the article, you should have understood some basic manipulations, but there are many. In this configuration, spark master is yarn, mesos or standalone. Why use Spark?. In this API Testing tutorial, we take a look at how to parse JSON response and extract information using REST-Assured library. CSV to Parquet. Parquet Files Parquet. Let’s read a JSON file, parse it and convert it to CSV file. For this type of intermediate data, we generally use the Avro file format. First option is quicker but specific to Jupyter Notebook, second option is a broader approach to get PySpark available in your favorite IDE. sql and we want to import SparkSession … and then we want to create a spark context … which is the variable again that gives us a reference point. And now you check its first. The image below depicts the performance of Spark SQL when compared to Hadoop. We will convert csv files to parquet format using Apache Spark. Although the file that is used here is not a typical JSON file. You can process a fie at a time. The first is command line options such as --master and Zeppelin can pass these options to spark-submit by exporting SPARK_SUBMIT_OPTIONS in conf/zeppelin-env. Apache Spark is a modern processing engine that is focused on in-memory processing. Fast Data Analytics with Spark and Python 1. I want to read the contents of all the A. PySpark Professional Training : Including HandsOn Sessions Multiple Reasons behind Spark High Performance. 04 - Embedded & Distributed Apache Drill - Query File System, JSON, and Parquet Apache Drill - HBase query Apache Drill - Hive query Apache Drill - MongoDB query. /parquet file path). Apache Spark is a component of IBM Open Platform with Apache Spark and Apache Hadoop that includes Apache Spark. Apache Spark 1. You can vote up the examples you like or vote down the ones you don't like. In this Spark Tutorial - Read Text file to RDD, we have learnt to read data from a text file to an RDD using SparkContext. The first will deal with the import and export of any type of data, CSV , text file…. class pyspark. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. json users may end up with multiple Parquet files with different but mutually compatible schemas. I've tried sc. 6, so I was using the Databricks CSV reader; in Spark 2 this is now available natively. NOTES: Like shapely, these spatial data types are limited to discrete entities/features and do not address continuously varying rasters or fields. For this type of intermediate data, we generally use the Avro file format. Let us understand the essentials to develop Spark 2 based Data Engineering Applications using Python 3 as Programming Language. The requirement is to process these data using the Spark data frame. This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. Interacting with HBase from PySpark. text("people. Introduction to [a]Spark / PySpark. zip files (versions might vary depending on the Spark version) are necessary to run a Python script in Spark. Assuming, have some knowledge on Apache Parquet file format, DataFrame APIs and basics of Python and Scala. JSON file is one big multiline file which has more than one JSON object but each object spawns multiple lines; One big JSON object spawning multiple lines in a file. csv files into an RDD?. The MapR Database OJAI Connector for Apache Spark makes it easier to build real-time or batch pipelines between your JSON data and MapR Database and leverage Spark within the pipeline. toJavaRDD(). Just pass a path-to-a-directory / path-with-wildcards instead of path to a single file. textFile("hdfs:///data/*. You want to open a plain-text file in Scala and process the lines in that file. We are excited to introduce the integration of HDInsight PySpark into Visual Studio Code (VSCode), which allows developers to easily edit Python scripts and submit PySpark statements to HDInsight clusters. Then, we'll read in back from the file and play with it. File Formats : Spark provides a very simple manner to load and save data files in a very large number of file formats. I'm trying to utilize Spark/PySpark (version 1. csv method to load the data into a DataFrame,. All types are assumed to be string. Simple example of processing twitter JSON payload from a Kafka stream with Spark Streaming in Python - 01_Spark+Streaming+Kafka+Twitter. Having gone through the process myself, I've documented my steps and share the knowledge, hoping it will save some time and frustration for some of you. Personally I would go with Python UDF and wouldn't bother with anything else: Vectors are not native SQL types so there will be performance overhead one way or another. The first will deal with the import and export of any type of data, CSV , text file…. spark_read_json: Read a JSON file into a Spark DataFrame in sparklyr: R Interface to Apache Spark rdrr. ERR_FSPROVIDER_INVALID_FILE_NAME: Invalid file name ERR_SPARK_PYSPARK_CODE_FAILED_UNSPECIFIED: Pyspark code failed If the column contains a JSON object which. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). In this article, you use Jupyter Notebook available with HDInsight Spark clusters to run a job that reads data from a Data Lake Storage account. It will help you in setting up your environment and get access to the latest stable version of Apache Spark. When several consecutive recipes in a DSS Flow (including with branches or splits) use the Spark engine, DSS can automatically merge all of these recipes and run them as a single Spark job, called a Spark pipeline. After the reading the parsed data in, the resulting output is a Spark DataFrame. Importing Data into Hive Tables Using Spark. 2 with PySpark (Spark Python API) Wordcount using CDH5 Apache Spark 1. $\begingroup$ This does not directly answer the question, but here I give a suggestion to improve the naming method so that in the end, we don't have to type, for example: [td1, td2, td3, td4, td5, td6, td7, td8, td9, td10]. session(sparkPackages = "com. How to Store and Query JSON Objects. key or any of the methods outlined in the aws-sdk documentation Working with AWS. 1) to parse through and reduce this data, but I can't figure out the right way to load it into an RDD, because it's neither all records > one file (in which case I'd use sc. Try Custom Input Format and Record Reader. Conclusion : In this Spark Tutorial – Write Dataset to JSON file, we have learnt to use write() method of Dataset class and export the data to a JSON file using json() method. we will use | for or, & for and , ! for not. Line 16) I save data as CSV files in "users_csv" directory. I will need to take these bytes, convert to string, and parse that string into a JSON object that I can work with in Spark.