• Parquet: Open-source columnar format for Hadoop (1 of 6)

    published: 21 Nov 2014
  • Parquet: Open-source columnar format for Hadoop (3 of 6)

    published: 21 Nov 2014
  • Parquet: Open-source columnar format for Hadoop (2 of 6)

    published: 21 Nov 2014
  • Parquet: Open-source columnar format for Hadoop (4 of 6)

    published: 21 Nov 2014
  • Parquet: Open-source columnar format for Hadoop (6 of 6)

    published: 21 Nov 2014
  • Parquet: Open-source columnar format for Hadoop (5 of 6)

    published: 21 Nov 2014
  • The columnar roadmap Apache Parquet and Apache Arrow

    published: 20 Jun 2017
  • Parquet Format at Twitter

    Julien Le Dem discusses Parquet, a columnar file format for Hadoop. Performance and compression benefits of using columnar storage formats for storing and processing large amounts of data are well documented in academic literature as well as several commercial analytical databases. Parquet supports deeply nested structures, efficient encoding and column compression schemes, and is designed to be compatible with a variety of higher-level type systems. Its integration in most of the Hadoop processing frameworks (Impala, Hive, Pig, Cascading, Crunch, Scalding, Spark, ...) and serialization models (Thrift, Avro, Protocol Buffers, ...) makes it easy to use in existing ETL and processing pipelines, while giving flexibility of choice on the query engine (whether in Java or C++). Join the conver...

    published: 18 Apr 2014
  • UNILIN production process parquet

    Take a look behind the scenes and find out how UNILIN manufactures its parquet hardwood floors. In this 30-minute explanatory movie, you follow a piece of wood as it travels through the factories in Czech and Malaysia and is being transformed from tree trunk to finished, ready-to-use hardwood floor.

    published: 18 Nov 2015
  • Pydata Paris 2016 - How Apache Arrow and Parquet boost cross-language interop

    How Apache Arrow and Parquet boost cross-language interop by Uwe L. Korn (Blue Yonder)

    published: 01 Jul 2016
  • Spark + Parquet In Depth: Spark Summit East talk by: Emily Curtin and Robbie Strickland

    published: 14 Feb 2017
  • Apache Parquet: Parquet file internals and inspecting Parquet file structure

    In this video we will look at the inernal structure of the Apache Parquet storage format and will use the Parquet-tool to inspect the contents of the file. Apache Parquet is a columnar storage format available in the Hadoop ecosystem Related videos: Creating Parquet files using Apache Spark: https://youtu.be/-ra0pGUw7fo Parquet vs Avro: https://youtu.be/sLuHzdMGFNA

    published: 22 Apr 2017
  • #bbuzz 2016: Julien Le Dem - Efficient Data formats for Analytics with Parquet and Arrow

    Find more information here: https://berlinbuzzwords.de/session/efficient-data-formats-analytics-parquet-and-arrow Hadoop makes it relatively easy to store petabytes of data. However, storing data is not enough; columnar layouts for storage and in-memory execution allow the analysis of large amounts of data very quickly and efficiently. It provides the ability for multiple applications to share a common data representation and perform operations at full CPU throughput using SIMD and Vectorization. For interoperability, row based encodings - CSV, Thrift, Avro - combined with general purpose compression algorithms - GZip, LZO, Snappy - are common but inefficient. As discussed extensively in the database literature, a columnar layout with statistics and sorting provides vertical and horizonta...

    published: 12 Jun 2016
  • Uwe L Korn - Efficient and portable DataFrame storage with Apache Parquet

    Filmed at PyData London 2017 www.pydata.org Description Apache Parquet is the most used columnar data format in the big data processing space and recently gained Pandas support. It leverages various techniques to store data in a CPU and I/O efficient way and provides capabilities to push-down queries to the I/O layer. In this talk, it is shown how to use it in Python, detail its structure and present the portable usage with other tools. Abstract Since its creation in 2013, Apache Parquet has risen to be the most widely used binary columnar storage format in the big data processing space. While supporting basic attributes of a columnar format like reading a subset of columns, it also leverages techniques to store the data efficiently while providing fast access. In addition the format is ...

    published: 15 May 2017
  • 0605 Efficient Data Storage for Analytics with Parquet 2 0

    published: 23 Jun 2014
  • Even Faster When Presto meets Parquet @ Uber

    published: 20 Jun 2017
  • Apache Parquet & Apache Spark

    - Overview of Apache Parquet and key benefits of using Apache Parquet. - Demo of using Apache Spark with Apache Parquet

    published: 16 Jun 2016
  • File Format Benchmark Avro JSON ORC and Parquet

    published: 29 Jun 2016
  • Parquet Format at Criteo

    Criteo has petabyte scale data stored in HDFS with an analytic stack based on Cascading and Hive. Up until recently it was 100% backed by RCFile. In this presentation, Justin Coffey discusses how Criteo migrated to Parquet along with benchmarks of space and time comparisons vs RCFile. Join the conversation at http://twitter.com/university

    published: 21 Apr 2014
  • Parquet vs Avro

    In this video we will cover the pros-cons of 2 Popular file formats used in the Hadoop ecosystem namely Apache Parquet and Apache Avro Agenda: Where these formats are used Similarities Key Considerations when choosing: -Read vs Write Characteristics -Tooling -Schema Evolution General guidelines -Scenarios to keep data in both Parquet and Avro Avro is a row-based storage format for Hadoop. However Avro is more than a serialisation framework its also an IPC framework Parquet is a column-based storage format for Hadoop. Both highly optimised (vs pain text), both are self describing , uses compression If your use case typically scans or retrieves all of the fields in a row in each query, Avro is usually the best choice. If your dataset has many columns, and your use case typically inv...

    published: 16 Feb 2017
  • Using Apache Arrow, Calcite and Parquet to build a Relational Cache | DataEngConf NYC '17

    Download slides for this talk: https://goo.gl/eMWk8i Pre-register for DataEngConf SF '18: https://dataeng.co/2BDF1Li Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible. We'll start by talking about in-memory caches and the difference between block-based and data-aware caching strategies. We'll discuss the deployment desi...

    published: 04 Dec 2017
  • Hadoop Tutorial for Beginners - 32 Hive Storage File Formats: Sequence, RC, ORC, Avro, Parquet

    In this tutorial you will learn about Hive Storage File Formats, Sequence Files, RC File format, ORC File Format, Avro and Parquet

    published: 17 Feb 2017
  • Avro vs Parquet

    Hadoop File formats

    published: 18 Oct 2017
  • What is Avro?

    http://www.ibm.com/software/data/bigdata/ Avro defined in 3 minutes with Rafael Coss, manager Big Data Enablement for IBM. This is number eight in our series of 'What is...' videos. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free

    published: 25 Sep 2012
developed with YouTube
Parquet: Open-source columnar format for Hadoop (1 of 6)

Parquet: Open-source columnar format for Hadoop (1 of 6)

  • Order:
  • Duration: 15:01
  • Updated: 21 Nov 2014
  • views: 10415
videos
https://wn.com/Parquet_Open_Source_Columnar_Format_For_Hadoop_(1_Of_6)
Parquet: Open-source columnar format for Hadoop (3 of 6)

Parquet: Open-source columnar format for Hadoop (3 of 6)

  • Order:
  • Duration: 15:01
  • Updated: 21 Nov 2014
  • views: 3172
videos
https://wn.com/Parquet_Open_Source_Columnar_Format_For_Hadoop_(3_Of_6)
Parquet: Open-source columnar format for Hadoop (2 of 6)

Parquet: Open-source columnar format for Hadoop (2 of 6)

  • Order:
  • Duration: 15:01
  • Updated: 21 Nov 2014
  • views: 4627
videos
https://wn.com/Parquet_Open_Source_Columnar_Format_For_Hadoop_(2_Of_6)
Parquet: Open-source columnar format for Hadoop (4 of 6)

Parquet: Open-source columnar format for Hadoop (4 of 6)

  • Order:
  • Duration: 15:01
  • Updated: 21 Nov 2014
  • views: 2127
videos
https://wn.com/Parquet_Open_Source_Columnar_Format_For_Hadoop_(4_Of_6)
Parquet: Open-source columnar format for Hadoop (6 of 6)

Parquet: Open-source columnar format for Hadoop (6 of 6)

  • Order:
  • Duration: 22:02
  • Updated: 21 Nov 2014
  • views: 729
videos
https://wn.com/Parquet_Open_Source_Columnar_Format_For_Hadoop_(6_Of_6)
Parquet: Open-source columnar format for Hadoop (5 of 6)

Parquet: Open-source columnar format for Hadoop (5 of 6)

  • Order:
  • Duration: 15:01
  • Updated: 21 Nov 2014
  • views: 1043
videos
https://wn.com/Parquet_Open_Source_Columnar_Format_For_Hadoop_(5_Of_6)
The columnar roadmap  Apache Parquet and Apache Arrow

The columnar roadmap Apache Parquet and Apache Arrow

  • Order:
  • Duration: 42:41
  • Updated: 20 Jun 2017
  • views: 1120
videos
https://wn.com/The_Columnar_Roadmap_Apache_Parquet_And_Apache_Arrow
Parquet Format at Twitter

Parquet Format at Twitter

  • Order:
  • Duration: 23:45
  • Updated: 18 Apr 2014
  • views: 9339
videos
Julien Le Dem discusses Parquet, a columnar file format for Hadoop. Performance and compression benefits of using columnar storage formats for storing and processing large amounts of data are well documented in academic literature as well as several commercial analytical databases. Parquet supports deeply nested structures, efficient encoding and column compression schemes, and is designed to be compatible with a variety of higher-level type systems. Its integration in most of the Hadoop processing frameworks (Impala, Hive, Pig, Cascading, Crunch, Scalding, Spark, ...) and serialization models (Thrift, Avro, Protocol Buffers, ...) makes it easy to use in existing ETL and processing pipelines, while giving flexibility of choice on the query engine (whether in Java or C++). Join the conversation at http://twitter.com/university
https://wn.com/Parquet_Format_At_Twitter
UNILIN production process parquet

UNILIN production process parquet

  • Order:
  • Duration: 28:35
  • Updated: 18 Nov 2015
  • views: 10847
videos
Take a look behind the scenes and find out how UNILIN manufactures its parquet hardwood floors. In this 30-minute explanatory movie, you follow a piece of wood as it travels through the factories in Czech and Malaysia and is being transformed from tree trunk to finished, ready-to-use hardwood floor.
https://wn.com/Unilin_Production_Process_Parquet
Pydata Paris 2016 - How Apache Arrow and Parquet boost cross-language interop

Pydata Paris 2016 - How Apache Arrow and Parquet boost cross-language interop

  • Order:
  • Duration: 28:42
  • Updated: 01 Jul 2016
  • views: 243
videos
How Apache Arrow and Parquet boost cross-language interop by Uwe L. Korn (Blue Yonder)
https://wn.com/Pydata_Paris_2016_How_Apache_Arrow_And_Parquet_Boost_Cross_Language_Interop
Spark + Parquet In Depth: Spark Summit East talk by: Emily Curtin and Robbie Strickland

Spark + Parquet In Depth: Spark Summit East talk by: Emily Curtin and Robbie Strickland

  • Order:
  • Duration: 29:50
  • Updated: 14 Feb 2017
  • views: 7850
videos
https://wn.com/Spark_Parquet_In_Depth_Spark_Summit_East_Talk_By_Emily_Curtin_And_Robbie_Strickland
Apache Parquet: Parquet file internals and inspecting Parquet file structure

Apache Parquet: Parquet file internals and inspecting Parquet file structure

  • Order:
  • Duration: 24:38
  • Updated: 22 Apr 2017
  • views: 3438
videos
In this video we will look at the inernal structure of the Apache Parquet storage format and will use the Parquet-tool to inspect the contents of the file. Apache Parquet is a columnar storage format available in the Hadoop ecosystem Related videos: Creating Parquet files using Apache Spark: https://youtu.be/-ra0pGUw7fo Parquet vs Avro: https://youtu.be/sLuHzdMGFNA
https://wn.com/Apache_Parquet_Parquet_File_Internals_And_Inspecting_Parquet_File_Structure
#bbuzz 2016: Julien Le Dem -  Efficient Data formats for Analytics with Parquet and Arrow

#bbuzz 2016: Julien Le Dem - Efficient Data formats for Analytics with Parquet and Arrow

  • Order:
  • Duration: 45:53
  • Updated: 12 Jun 2016
  • views: 415
videos
Find more information here: https://berlinbuzzwords.de/session/efficient-data-formats-analytics-parquet-and-arrow Hadoop makes it relatively easy to store petabytes of data. However, storing data is not enough; columnar layouts for storage and in-memory execution allow the analysis of large amounts of data very quickly and efficiently. It provides the ability for multiple applications to share a common data representation and perform operations at full CPU throughput using SIMD and Vectorization. For interoperability, row based encodings - CSV, Thrift, Avro - combined with general purpose compression algorithms - GZip, LZO, Snappy - are common but inefficient. As discussed extensively in the database literature, a columnar layout with statistics and sorting provides vertical and horizontal partitioning, thus keeping IO to a minimum. Additionally a number of key big data technologies have or will soon have in-memory columnar capabilities. This includes Kudu, Ibis and Drill. Sharing a common in-memory columnar representation allows interoperability without the usual cost of serialization. Understanding modern CPU architecture is critical to maximizing processing throughput. We’ll discuss the advantages of columnar layouts in Parquet and Arrow for in-memory processing and data encodings used for storage - dictionary, bit-packing, prefix coding. We’ll dissect and explain the design choices that enable us to achieve all three goals of interoperability, space and query efficiency. In addition, we’ll provide an overview of what’s coming in Parquet and Arrow in the next year.
https://wn.com/Bbuzz_2016_Julien_Le_Dem_Efficient_Data_Formats_For_Analytics_With_Parquet_And_Arrow
Uwe L  Korn - Efficient and portable DataFrame storage with Apache Parquet

Uwe L Korn - Efficient and portable DataFrame storage with Apache Parquet

  • Order:
  • Duration: 28:31
  • Updated: 15 May 2017
  • views: 550
videos
Filmed at PyData London 2017 www.pydata.org Description Apache Parquet is the most used columnar data format in the big data processing space and recently gained Pandas support. It leverages various techniques to store data in a CPU and I/O efficient way and provides capabilities to push-down queries to the I/O layer. In this talk, it is shown how to use it in Python, detail its structure and present the portable usage with other tools. Abstract Since its creation in 2013, Apache Parquet has risen to be the most widely used binary columnar storage format in the big data processing space. While supporting basic attributes of a columnar format like reading a subset of columns, it also leverages techniques to store the data efficiently while providing fast access. In addition the format is structured in such a fashion that when supplied to a query engine, Parquet provides indexing hints and statistics to quickly skip over chunks of irrelevant data. In recent months, efficient implementations to load and store Parquet files in Python became available, bringing the efficiency of the format to Pandas DataFrames. While this provides a new option to store DataFrames, it especially allows us to share data between Pandas and a lot of other popular systems like Apache Spark or Apache Impala. In this talk we will show the improvements that Parquet bring performance-wise but also will highlight important aspects of the format that make it portable and efficient for queries on large amount of data. As not all features are yet available in Python, an overview of the upcoming Python-specific improvements and how the Parquet format will be extended in general is given at the end of the talk. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
https://wn.com/Uwe_L_Korn_Efficient_And_Portable_Dataframe_Storage_With_Apache_Parquet
0605 Efficient Data Storage for Analytics with Parquet 2 0

0605 Efficient Data Storage for Analytics with Parquet 2 0

  • Order:
  • Duration: 41:59
  • Updated: 23 Jun 2014
  • views: 52674
videos
https://wn.com/0605_Efficient_Data_Storage_For_Analytics_With_Parquet_2_0
Even Faster  When Presto meets Parquet @ Uber

Even Faster When Presto meets Parquet @ Uber

  • Order:
  • Duration: 36:23
  • Updated: 20 Jun 2017
  • views: 242
videos
https://wn.com/Even_Faster_When_Presto_Meets_Parquet_Uber
Apache Parquet & Apache Spark

Apache Parquet & Apache Spark

  • Order:
  • Duration: 13:43
  • Updated: 16 Jun 2016
  • views: 7524
videos
- Overview of Apache Parquet and key benefits of using Apache Parquet. - Demo of using Apache Spark with Apache Parquet
https://wn.com/Apache_Parquet_Apache_Spark
File Format Benchmark Avro JSON ORC and Parquet

File Format Benchmark Avro JSON ORC and Parquet

  • Order:
  • Duration: 39:59
  • Updated: 29 Jun 2016
  • views: 4322
videos
https://wn.com/File_Format_Benchmark_Avro_Json_Orc_And_Parquet
Parquet Format at Criteo

Parquet Format at Criteo

  • Order:
  • Duration: 9:34
  • Updated: 21 Apr 2014
  • views: 1005
videos
Criteo has petabyte scale data stored in HDFS with an analytic stack based on Cascading and Hive. Up until recently it was 100% backed by RCFile. In this presentation, Justin Coffey discusses how Criteo migrated to Parquet along with benchmarks of space and time comparisons vs RCFile. Join the conversation at http://twitter.com/university
https://wn.com/Parquet_Format_At_Criteo
Parquet vs Avro

Parquet vs Avro

  • Order:
  • Duration: 13:28
  • Updated: 16 Feb 2017
  • views: 5592
videos
In this video we will cover the pros-cons of 2 Popular file formats used in the Hadoop ecosystem namely Apache Parquet and Apache Avro Agenda: Where these formats are used Similarities Key Considerations when choosing: -Read vs Write Characteristics -Tooling -Schema Evolution General guidelines -Scenarios to keep data in both Parquet and Avro Avro is a row-based storage format for Hadoop. However Avro is more than a serialisation framework its also an IPC framework Parquet is a column-based storage format for Hadoop. Both highly optimised (vs pain text), both are self describing , uses compression If your use case typically scans or retrieves all of the fields in a row in each query, Avro is usually the best choice. If your dataset has many columns, and your use case typically involves working with a subset of those columns rather than entire records, Parquet is optimized for that kind of work. Finally in the video we will cover cases where you may use both file formats
https://wn.com/Parquet_Vs_Avro
Using Apache Arrow, Calcite and Parquet to build a Relational Cache | DataEngConf NYC '17

Using Apache Arrow, Calcite and Parquet to build a Relational Cache | DataEngConf NYC '17

  • Order:
  • Duration: 43:07
  • Updated: 04 Dec 2017
  • views: 415
videos
Download slides for this talk: https://goo.gl/eMWk8i Pre-register for DataEngConf SF '18: https://dataeng.co/2BDF1Li Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible. We'll start by talking about in-memory caches and the difference between block-based and data-aware caching strategies. We'll discuss the deployment design of this type of solution as well as cover the strengths of each. There will also be a discussion of the relationship of security and predicate application in these scenarios. Then we'll go into detail about how columnar storage formats can further enhance performance by minimizing read time, optimizing for vectorized in-memory processing and powerful compression techniques. Lastly, we'll introduce a much more advanced way to speed access to data called relational caching. Relational caching builds a cache on columnar in-memory caching techniques but also includes a full comprehension of how data is being used and how different forms of data relate to each other. This will include leveraging multiple sorting and partitioning strategies as well as maintaining multiple related derivations of data for different types of access patterns. As part of this and we also cover approaches to data ttl, relational cache consistency and several different approaches to data mutation and real-time updates. Follow DataEngConf on: Twitter: https://twitter.com/dataengconf LinkedIn: https://www.linkedin.com/company/hakkalabs/ Facebook: https://web.facebook.com/hakkalabs
https://wn.com/Using_Apache_Arrow,_Calcite_And_Parquet_To_Build_A_Relational_Cache_|_Dataengconf_NYC_'17
Hadoop Tutorial for Beginners - 32 Hive Storage File Formats: Sequence, RC, ORC, Avro, Parquet

Hadoop Tutorial for Beginners - 32 Hive Storage File Formats: Sequence, RC, ORC, Avro, Parquet

  • Order:
  • Duration: 10:36
  • Updated: 17 Feb 2017
  • views: 2751
videos
In this tutorial you will learn about Hive Storage File Formats, Sequence Files, RC File format, ORC File Format, Avro and Parquet
https://wn.com/Hadoop_Tutorial_For_Beginners_32_Hive_Storage_File_Formats_Sequence,_Rc,_Orc,_Avro,_Parquet
Avro vs Parquet

Avro vs Parquet

  • Order:
  • Duration: 3:55
  • Updated: 18 Oct 2017
  • views: 142
videos https://wn.com/Avro_Vs_Parquet
What is Avro?

What is Avro?

  • Order:
  • Duration: 3:00
  • Updated: 25 Sep 2012
  • views: 23052
videos
http://www.ibm.com/software/data/bigdata/ Avro defined in 3 minutes with Rafael Coss, manager Big Data Enablement for IBM. This is number eight in our series of 'What is...' videos. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free
https://wn.com/What_Is_Avro
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