Python Write Parquet

This guide uses Avro 1. For example, you can read and write Parquet files using Pig and MapReduce jobs. About the Technology. It does not compile. fastparquet is a python implementation of the parquet format, aiming integrate into python-based big data work-flows. This post shows how to use Hadoop Java API to read and write Parquet file. Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do not differentiate between binary data and strings when writing out the Parquet schema. Interacting with Parquet on S3 with PyArrow and s3fs Write to Parquet on S3 notebook Python Jupyter S3 pyarrow s3fs Parquet. can you pleases explain how i can pass the path instead of File. parquet or sc. You will need to put following jars in class path in order to read and write Parquet files in Hadoop. e row oriented) and Parquet (i. How does Apache Spark read a parquet file. An Azure Databricks table is a collection of structured data. Parquet is a columnar storage format. parquet经常会生成太多的小文件,例如申请了100个block,而每个block中的结果. The parquet is only 30% of the size. This package aims to provide a performant library to read and write Parquet files from Python, without any need for a Python-Java bridge. Dask Dataframes can read and store data in many of the same formats as Pandas dataframes. How can I achieve this. Watch it together with the written tutorial to deepen your understanding: Reading and Writing CSV Files Let’s face it: you need to get information into and out of your programs through more than just the keyboard. 3 TB of data have been transferred between memory and CPU processed during the workload (read + write activity). Required Skills Programming using Scala or Python or both SQL and Data Modeling Data Processing using Apache Spark Data ingestion using Kafka Ability to build end to end pipelines Essential Skills Linux commands and Shell Scripting Big Data on Cloud (AWS EMR) Scheduling tools like Oozie, Azkaban, Airflow etc Ability to integrate with NoSQL. Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Want an easy way to either read or write Parquet files in Alteryx? Use Apache Arrow (more specifically PyArrow) and the Python Tool. Apache Spark integration. It does not compile. Imagine a simulator producing gigabytes of data per second. Parquet is columnar store format published by Apache. Reading/Writing Parquet files If you have built pyarrowwith Parquet support, i. The key function for working with files in Python is the open() function. Posts about Parquet written by in4maniac. Note that if you install node-parquet this way, you can still use it as a dependency module in your local projects by linking (npm link node-parquet) which avoids the cost of recompiling the complete parquet-cpp library and its dependencies. Parquet: Parquet is a columnar format that is supported by many other data processing systems, Spark SQL support for both reading and writing Parquet files that automatically preserves the schema of the original data. It can be very easy to use Spark to convert XML to Parquet and then query and analyse the output data. Dask can create DataFrames from various data storage formats like CSV, HDF, Apache Parquet, and others. So create a role along with the following policies. SnappyCodec Parquet File Read Write Apply compression while writing Supported compression codecs : none, gzip, lzo, snappy (default), uncompressed AVRO File Read Write Apply compression while writing. But one of the easiest ways here will be using Apache Spark and Python script (pyspark). Data Science in Python Training; Data Science in R Language Training; Salesforce Certification Training; NoSQL Database Training; Hadoop Admin Training. On the one hand, the Spark documentation touts Parquet as one of the best formats for analytics of big data (it is) and on the other hand the support for Parquet in Spark is incomplete and annoying to use. The supported compression types, the compression default, and how you specify compression depends on the CDH component writing the files. array Aug 16, 2019 Aug 21, 2019 Unassign ed Wes McKinne y OPEN Unresolved ARR OW-6253 [Python] Expose "enable_buffered_stream" option from parquet::ReaderProperties in pyarrow. Process Functions let me write custom aggregations without a lot of mental overhead. Spark File Format Showdown - CSV vs JSON vs Parquet Posted by Garren on 2017/10/09 Apache Spark supports many different data sources, such as the ubiquitous Comma Separated Value (CSV) format and web API friendly JavaScript Object Notation (JSON) format. mode("append") when writing the DataFrame. This post covers the basics of how to write data into parquet. The code tends to be concise, quick to write, and expressive. Parquet: Parquet is a columnar format that is supported by many other data processing systems, Spark SQL support for both reading and writing Parquet files that automatically preserves the schema of the original data. 0 release 4. Since it was developed as part of the Hadoop ecosystem, Parquet's reference implementation is written in Java. Columns of same date-time are stored together as rows in Parquet format, so as to offer better storage, compression and data retrieval. In this example we read and write data with the popular CSV and Parquet formats, and discuss best practices when using these formats. Working with parquet files CSV files are great for saving the contents of rectangular data objects (like R data. 在pyspark中,使用数据框的文件写出函数write. geeksforgeeks. H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. 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. Columnar on-disk storage format 2. It uses a trick to get around some MAXScript syntax that can’t be replicated in Python by wrapping the “as String” coercion in a function on the MAXscript layer, and then calling that from the pymxs. Giuliano Rapoz looks at how you can build on the concept of Structured Streaming with Databricks, and how it can be used in conjunction with Power BI & Cosmos DB enabling visualisation and advanced analytics of the ingested data. I'm using python though not scala. It is confusing to new users who want to read and write parquet files. Similar to read operation, create Configuration object followed by FileSystem object and Path object. Accessing Parquet Files From Spark SQL Applications; Building Spark Applications; Configuring Spark Applications; Running Spark Applications. , files) from storage entities called "S3 Buckets" in the cloud with ease for a relatively small cost. Understanding write-through, write-around and write-back caching (with Python) This post explains the three basic cache writing policies: write-through, write-around and write-back. Apache Parquet is built from the ground up with complex nested data structures in mind. Read and Write files on HDFS. To read Parquet files in Spark SQL, use the SQLContext. The code tends to be concise, quick to write, and expressive. str is for strings of bytes. column oriented) file formats are HDFS (i. Parquet & Spark. If not None, only these columns will be read from the file. Since 2001, Processing has promoted software literacy within the visual arts and visual literacy within technology. Databases and Tables. parq files ( size in 70~80kb). For the uninitiated, while file formats like CSV are row-based storage, Parquet (and OCR) are columnar in nature — it's designed from the ground up for efficient storage, compression and encoding, which means better performance. The moral of this story is, the Python API to Apache Arrow gives Python users access to efficient columnar storage formats (parquet), which can lead to substantial savings in I/O performance and. On the one hand, the Spark documentation touts Parquet as one of the best formats for analytics of big data (it is) and on the other hand the support for Parquet in Spark is incomplete and annoying to use. com is an independently owned forum and website dedicated to the 1 last update 2019/07/16 Jeep Gladiator JT. It depends on thrift (0. Watch it together with the written tutorial to deepen your understanding: Reading and Writing CSV Files Let's face it: you need to get information into and out of your programs through more than just the keyboard. Reading and Writing the Apache Parquet Format¶. Pandas is a powerful data analysis Python library that is built on top of numpy which is yet another library that let’s you create 2d and even 3d arrays of data in Python. Write and read parquet files in Scala / Spark. Using Drill from within Python scripts opens up a new world of data analysis capabilities by coupling the distributed query power of Drill with all of the Python open source modules and frameworks available like numpy and pandas. Parquet or ORC are essential and well established standards to manage real world enterprise data workloads. # Credentials for AWS in the normal. My program reads in a parquet file that contains server log data about requests made to our website. CSV Files When you only pay for the queries that you run, or resources like CPU and storage, it is important to look at optimizing the data those systems rely on. This process is described below. Needs to be accessible from the cluster. Hi, We have a large binary file, that we want to be able to search (do a range query on key). The Python parquet process is pretty simple since you can convert a pandas DataFrame directly to a pyarrow Table which can be written out in parquet format with pyarrow. This tutorial will give a detailed introduction to CSV's and the modules and classes available for reading and writing data to CSV files. path: The path to the file. Python (1) QA (1). About Parquet 1. Follow the steps below to convert a simple CSV into a Parquet file using Drill. View detail. The aim of the talk is to show how to integrate R and Python with Microsoft Power BI and Tableau. To read or write Parquet data, you need to include the Parquet format in the storage plugin format definitions. The following parameters configure the maximum number of databases, tablespaces, and filespaces allowed in a system. Since it was developed as part of the Hadoop ecosystem, Parquet's reference implementation is written in Java. I'd like to write out the DataFrames to Parquet, but would like to partition on a particular column. For best performance when exporting to HDFS, set size to be smaller than the HDFS block size. We came across similar situation we are using spark 1. As you can see, a row group is a segment of the Parquet file that holds serialized (and compressed!) arrays of column entries. That seems about right in my experince, and I've seen upwards of about 80% file compression when converting JSON files over to parquet with Glue. The parquet-rs project is a Rust library to read-write Parquet files. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large. parquet-python is a pure-python implementation (currently with only read-support) of the parquet format. Process Functions let me write custom aggregations without a lot of mental overhead. Dremio stores all the page headers in the Parquet footer. The parquet is only 30% of the size. 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. 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. Starting Scala Spark - Read write to parquet file. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. This post covers the basics of how to write data into parquet. My program reads in a parquet file that contains server log data about requests made to our website. Some of the areas where they have been used include: importing and exporting customer data. parquet经常会生成太多的小文件,例如申请了100个block,而每个block中的结果. Parquet is a columnar format, supported by many data processing systems. There are many programming language APIs that have been implemented to support writing and reading parquet files. Spark can read/write data to Apache Hadoop using Hadoop {Input,Output}Formats. You will need to put following jars in class path in order to read and write Parquet files in Hadoop. The open() function takes two parameters; filename, and mode. Read and write in parquet format in Python. An R interface to Spark. The parquet package is unmaintained and outdated. Time Characteristics are really nice in Flink. The GzipFile class reads and writes gzip-format files, automatically compressing or decompressing the data so that it looks like an ordinary file object. Data Virtuality Pipes is an easy to use data integration tool. Lab 4: Using parquet-tools. NET that enables the reading and writings of Parquet files inside the. Doing this manually can be a bit tedious, specially if there are many files to upload located in different folders. If the data is a multi-file collection, such as generated by hadoop, the filename to supply is either the directory name, or the "_metadata" file contained therein - these are handled transparently. Dask Dataframes can read and store data in many of the same formats as Pandas dataframes. Python has been used to write all, or parts of, popular software projects like dnf/yum, OpenStack, OpenShot, Blender, Calibre, and even the original BitTorrent client. It would be even better if we could run Python script. Watch Now This tutorial has a related video course created by the Real Python team. parquet-python has been tested on python 2. And since Arrow is so closely related to parquet-cpp, support for Parquet output (again, from Python) is baked-in. This guide uses Avro 1. parquet-python. Write operation on HDFS In write operation ,we create a file in HDFS and copy content form source file which is available in local file system. 0 Here is the full working demo in Spark 1. The Azure Databricks Python Activity in a Data Factory pipeline runs a Python file in your Azure Databricks cluster. The crawlers needs read access of the S3, but save the Parquet files, it needs the Write access too. Apache Parquet vs. StructType(). phData is a fan of simple examples. The advanced notebook workflow notebooks demonstrate how to use these constructs. Union two DataFrames; Write the unioned DataFrame to a Parquet file; Read a DataFrame from the Parquet file; Explode the employees column; Use filter() to return the rows that match a predicate; The where() clause is equivalent to filter(). You will learn to: Print the metadata and schema for a Parquet file; View column-level compression ratios. Parquet format support for direct import from Azure Blob. Is there any limitation to the amount of data…i. Comparing ORC vs Parquet Data Storage Formats using Hive CSV is the most familiar way of storing the data. In the above examples, we have read and written the file on the local file system. format option to set the CTAS output format of a Parquet row group at the session or system level. gz, and install via python setup. Avro implementations for C, C++, C#, Java, PHP, Python, and Ruby can be downloaded from the Apache Avro™ Releases page. The parquet-rs project is a Rust library to read-write Parquet files. Generate data to use for reading and writing in parquet format. We are thrilled to introduce support for Azure Data Lake (ADL) Python and R extensions within Visual Studio Code (VSCode). The scripts can be used to manipulate data and even to generate visualizations. As I have outlined in a previous post, XML processing can be painful especially when you need to convert large volumes of complex XML files. Apache Parquet is a popular columnar storage format which stores its data as a bunch of files. can you pleases explain how i can pass the path instead of File. array Aug 16, 2019 Aug 21, 2019 Unassign ed Wes McKinne y OPEN Unresolved ARR OW-6253 [Python] Expose "enable_buffered_stream" option from parquet::ReaderProperties in pyarrow. In the previous blog, we looked at on converting the CSV format into Parquet format using Hive. Then you execute the DDL or DML statements from Hive. path: The path to the file. Now we have data in PARQUET table only, so actually, we have decreased the file size and stored in hdfs which definitely helps to reduce cost. It's commonly used in Hadoop ecosystem. 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. NET Standand 1. Step 5: View the Binary Parquet File (meetup_parquet. x branch of pymssql is built on the latest release of FreeTDS which removes many of the limitations found with older FreeTDS versions and the 1. For Python, the answer is "Arrow", in the form of the pyarrow package. Type: Bug. In order to understand Parquet file format in Hadoop better, first let’s see what is columnar format. Step 5: View the Binary Parquet File (meetup_parquet. I have some. I'm having trouble finding a library that allows Parquet files to be written using Python. The crawlers needs read access of the S3, but save the Parquet files, it needs the Write access too. Clearly we can't put everything neatly into a Python list first and then start munching — we must process the information as it comes in. In case you have any questions about the concepts explained here, please write a comment below. Our first problem was that row groups in our dataset were much larger than expected, causing issues such as out-of-memory. Distributed on NuGet, Parquet. 今回は、最近知った Apache Parquet フォーマットというものを Python で扱ってみる。 これは、データエンジニアリングなどの領域でデータを永続化するのに使うフォーマットになっている。. We plan to use Spark SQL to query this file in a distributed cluster. Parquet & Spark. values() to S3 without any need to save parquet locally. Easily organize, use, and enrich data — in real time, anywhere. Tables are equivalent to Apache Spark DataFrames. My program reads in a parquet file that contains server log data about requests made to our website. Above code will create parquet files in input-parquet directory. If you are visiting this page via google search, you already know what Parquet is. BZip2Codec org. Python (1) QA (1). The parquet package is unmaintained and outdated. Just figured that parquet writing method works for orc and json as well. Parquet形式への変換はいくつか方法がありますが、今回はPythonを使って行います。 ファイルを圧縮し、さらに Apache Parquet などの列形式に変換した場合、サイズは 3 分の 1 に圧縮され、Amazon S3 でのデータは最終的に 1 TB になります。. map() function. HISTORY: take the poll first before you read on How do you write Python path strings? I am sure everyone is sick of hearing check your filenames and paths and make sure there is no X or Y. This package aims to provide a performant library to read and write Parquet files from Python, without any need for a Python-Java bridge. As you can see, a row group is a segment of the Parquet file that holds serialized (and compressed!) arrays of column entries. Example Spark. [write a python vpn server best vpn for school] , write a python vpn server > Get nowhow to write a python vpn server for Jeepgladiatorforum. SQLContext(). The Pandas data-frame, df will contain all columns in the target file, and all row-groups concatenated together. DataFrame Parquet support. 2016 there seems to be NO python-only library capable of writing Parquet files. parquet-python has been tested on python 2. This post shows how to use reticulate to create parquet files directly from R. iterrows() and 27 times faster that. The dfs plugin definition includes the Parquet format. Spark is often an order(s) of magnitude faster than Hadoop for. An R interface to Spark. 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. parquet经常会生成太多的小文件,例如申请了100个block,而每个block中的结果. July 2013: 1. Avro acts as a data serialize and DE-serialize framework while parquet acts as a columnar storage so as to store the records in an optimized way. Arguments; A Spark DataFrame or dplyr operation. But it lacks may features of pandas but considering that you would preprocess the data only once in a while but aggregate it quite often, bcolz is really good. Thanks Arun for consolidating all the file formats. Your source code remains pure Python while Numba handles the compilation at runtime. Apache Parquet is built from the ground up with complex nested data structures in mind. Hive can actually use different backends for a. An Azure Databricks database is a collection of tables. NET is running (Android, iOS, IOT). The open() function takes two parameters; filename, and mode. XML Word Printable JSON. Python (1) QA (1). NET Standand 1. Ensure the code does not create a large number of partition columns with the datasets otherwise the overhead of the metadata can cause significant slow downs. 7, but should be mostly also compatible with Python 3. 0 release 4. Spark can read/write data to Apache Hadoop using Hadoop {Input,Output}Formats. In this article we will focus on how to use Amzaon S3 for regular file handling operations using Python and Boto library. 4 with Python 3 - Assessment Summary Databricks Certified Associate Developer for Apache Spark 2. SSIS is a remarkably efficient and powerful tool for importing data into SQL Server, but there are times when it is more convenient to use Python to handle non-standard text files due to familiarity with Python or compatibility with preexisting code. Writing a Pandas DataFrame into a Parquet file is equally simple, though one caveat to mind is the parameter timestamps_to_ms=True: This tells the PyArrow library to convert all timestamps from nanosecond precision to millisecond precision as Pandas only supports nanoseconds timestamps and deprecates the (kind of special) nanosecond precision timestamp in Parquet. I think it is pretty self-explanatory, the only parts that might not be is that we add some etl fields for tracking, and we cast the accessing device to one of a set of choices to make reporting easier (accomplished through the switch sql. The default io. 1, the latest version at the time of writing. As an example, we have recently been working on Parquet’s C++ implementation to provide an Arrow-Parquet toolchain for native code consumers like Python and R. Giuliano Rapoz looks at how you can build on the concept of Structured Streaming with Databricks, and how it can be used in conjunction with Power BI & Cosmos DB enabling visualisation and advanced analytics of the ingested data. 9) and python-snappy (for snappy compressed files). Any additional kwargs are passed. What is Row Oriented Storage Format? In row oriented storage, data is stored row wise on to the disk. Python File Handling Python Read Files Python Write/Create Files Python Delete Files Python MySQL MySQL Get Started MySQL Create Database MySQL Create Table MySQL Insert MySQL Select MySQL Where MySQL Order By MySQL Delete MySQL Drop Table MySQL Update MySQL Limit MySQL Join Python MongoDB. SQLContext(). Text File Read Write Apply compression while writing Supported compression codecs : org. We're the creators of MongoDB, the most popular database for modern apps, and MongoDB Atlas, the global cloud database on AWS, Azure, and GCP. It requires a number of inputs to make the API call and identify the correct script. pyarrow is a first class citizen in the Arrow project: a good deal of time and effort has been spent implementing the features on the Arrow roadmap. Keith Galli 140,049 views. Data Science in Python Training; Data Science in R Language Training; Salesforce Certification Training; NoSQL Database Training; Hadoop Admin Training. If you are running on a Hadoop client machine (like an edge node), you can use Spark Code or Python Code to read the data into a DataFrame and then pass that to the Apache Spark Code tool or the Python tool in Designer. This creates outputDir directory and stores, under it, all the part files created by the reducers as parquet files. As an example, we have recently been working on Parquet’s C++ implementation to provide an Arrow-Parquet toolchain for native code consumers like Python and R. Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Parquet: Parquet is a columnar format that is supported by many other data processing systems, Spark SQL support for both reading and writing Parquet files that automatically preserves the schema of the original data. parquet-python is a pure-python implementation (currently with only read-support) of the parquet format. com DataCamp Learn Python for Data Science Interactively Initializing SparkSession Spark SQL is Apache Spark's module for working with structured data. This command lists all the files in the directory, creates a Delta Lake transaction log that tracks these files, and automatically infers the data schema by reading the footers of all Parquet files. Watch it together with the written tutorial to deepen your understanding: Reading and Writing CSV Files Let's face it: you need to get information into and out of your programs through more than just the keyboard. Starting Scala Spark - Read write to parquet file. I have been wanting to write this post since the first time I conducted an Apache Spark workshop with Maria Mestre (her blog can be found here) and later with Erik Pazos. [Python] Write nanosecond timestamps using new NANO LogicalType Parquet unit. parquet file and write the selected columns from that table to namesAndFavColors. Because it is a Python object, None cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type 'object' (i. 7, but should be mostly also compatible with Python 3. Python was named as a favourite tool for data science by 45% of data scientists in 2016. The Python parquet process is pretty simple since you can convert a pandas DataFrame directly to a pyarrow Table which can be written out in parquet format with pyarrow. We will discuss on how to work with AVRO and Parquet files in Spark. To read Parquet files in Spark SQL, use the SQLContext. To make programming faster, Spark provides clean, concise APIs in Scala, Java and Python. In this example, the select API is used explicitly to select the fields of the file. Parquet, and other columnar formats handle a common Hadoop situation very efficiently. In order to understand Parquet file format in Hadoop better, first let’s see what is columnar format. Thus far the only method I have found is using Spark with the pyspark. is a columnar file format that provides optimizations to speed up queries and is a far more efficient file format than CSV or JSON. Net is easy to get started with, and is ready to empower your Big Data applications from your enterprise. Distributed on NuGet, Parquet. Parquet, and other columnar formats handle a common Hadoop situation very efficiently. How can I write a parquet file using Spark (pyspark)? I'm pretty new in Spark and I've been trying to convert a Dataframe to a parquet file in Spark but I haven't had success yet. Source to read data from a file. It's commonly used in Hadoop ecosystem. Avro acts as a data serialize and DE-serialize framework while parquet acts as a columnar storage so as to store the records in an optimized way. Python was named as a favourite tool for data science by 45% of data scientists in 2016. fastparquet. View detail. Step 5: View the Binary Parquet File (meetup_parquet. Parquet is a columnar format, supported by many data processing systems. But wait, there’s more!. map() function. The documentation says that I can use write. Imagine a simulator producing gigabytes of data per second. The code tends to be concise, quick to write, and expressive. Apache Spark integration. Python - How can I write a parquet file using Spark Stackoverflow. Arrow and Parquet are thus companion projects. If you only need to read Parquet files there is python-parquet. 4 • Part of the core distribution since 1. About the Technology. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Fall 2016: Python & C++ support 6. Parquet is a popular column-oriented storage format that can store records with nested fields efficiently. We will continue to use the Uber CSV source file as used in the Getting Started with Spark and Python tutorial presented earlier. 7 (jessie) Description I was testing writing DataFrame to partitioned Parquet files. Lab 4: Using parquet-tools. Write a Spark DataFrame to a Parquet file. When using local file APIs, you must provide the path under /dbfs. July 2013: 1. We just need to follow this process through reticulate in R:. parquet-python has been tested on python 2. Hadoop Distributed File…. I'm pretty new in Spark and I've been trying to convert a Dataframe to a parquet file in Spark but I haven't had success yet. [Python] Unable to write StructArrays with multiple children to parquet. mode("append") when writing the DataFrame. The one advantage that Parquet includes over something like Arrow, at least as far as I understand the implementations currently, is that Parquet includes native support for compression. This post shows how to use reticulate to create parquet files directly from R. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Master Python loops to deepen your knowledge. Then write the header the the output VCF file then write the dataframe to the same file with the mode options set to 'a' to append to the end of the file. Comparing ORC vs Parquet Data Storage Formats using Hive CSV is the most familiar way of storing the data. Write to Parquet File in Python. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons. Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. frame s and Spark DataFrames ) to disk. JournalDev is a great platform for Java Developers.