Apache Hadoop HDFS
HDFS (Hadoop Distributed File System) is a highly available, distributed file system for storing large amounts of data. Data is stored on several computers (nodes) within a cluster. This is done by dividing the files into data blocks of fixed length and distributing them redundantly across the nodes.
The HDFS architecture is composed of master and worker nodes. The master node, called NameNode, is responsible for processing all incoming requests and organizes the storage of files and their associated metadata in the worder nodes, called DataNodes. HDFS is one of the main components of the Hadoop framework.
- Learn more
- Official website
Related articles
H2O in practice: a protocol combining AutoML with traditional modeling approaches
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python, XGBoost
H20 comes with a lot of functionalities. The second part of the series H2O in practice proposes a protocol to combine AutoML modeling with traditional modeling and optimization approach. The objectiveā¦
Nov 12, 2021
Internship in Big Data infrastructure with TDP
Categories: Infrastructure, Learning | Tags: Cyber Security, DevOps, Java, Hadoop, IaC, Internship, TDP
Job Description Big Data and distributed computing is at Adaltasā core. We support our partners in the deployment, maintenance and optimization of some of Franceās largest clusters. Adaltas is also anā¦
By Daniel HARTY
Oct 25, 2021
H2O in practice: a Data Scientist feedback
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python
Automated machine learning (AutoML) platforms are gaining popularity and becoming a new important tool in the data scientistsā toolbox. A few months ago, I introduced H2O, an open-source platform forā¦
Sep 29, 2021
Storage size and generation time in popular file formats
Categories: Data Engineering, Data Science | Tags: Avro, HDFS, Hive, ORC, Parquet, Big Data, Data Lake, File Format, JavaScript Object Notation (JSON)
Choosing an appropriate file format is essential, whether your data transits on the wire or is stored at rest. Each file format comes with its own advantages and disadvantages. We covered them in aā¦
Mar 22, 2021
Faster model development with H2O AutoML and Flow
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python
Building Machine Learning (ML) models is a time-consuming process. It requires expertise in statistics, ML algorithms, and programming. On top of that, it also requires the ability to translate aā¦
Dec 10, 2020
Connecting to ADLS Gen2 from Hadoop (HDP) and Nifi (HDF)
Categories: Big Data, Cloud Computing, Data Engineering | Tags: NiFi, Hadoop, HDFS, Authentication, Authorization, Azure, Azure Data Lake Storage (ADLS), OAuth2
As data projects built in the Cloud are becoming more and more frequent, a common use case is to interact with Cloud storage from an existing on premise Big Data platform. Microsoft Azure recentlyā¦
Nov 5, 2020
Installing Hadoop from source: build, patch and run
Categories: Big Data, Infrastructure | Tags: Maven, Java, LXD, Hadoop, HDFS, Docker, TDP, Unit tests
Commercial Apache Hadoop distributions have come and gone. The two leaders, Cloudera and Hortonworks, have merged: HDP is no more and CDH is now CDP. MapR has been acquired by HP and IBM BigInsightsā¦
Aug 4, 2020
Download datasets into HDFS and Hive
Categories: Big Data, Data Engineering | Tags: Business intelligence, Data Engineering, Data structures, Database, Hadoop, HDFS, Hive, Big Data, Data Analytics, Data Lake, Data lakehouse, Data Warehouse
Introduction Nowadays, the analysis of large amounts of data is becoming more and more possible thanks to Big data technology (Hadoop, Spark,ā¦). This explains the explosion of the data volume and theā¦
By Aida NGOM
Jul 31, 2020
Comparison of different file formats in Big Data
Categories: Big Data, Data Engineering | Tags: Business intelligence, Data structures, Avro, HDFS, ORC, Parquet, Batch processing, Big Data, CSV, JavaScript Object Notation (JSON), Kubernetes, Protocol Buffers
In data processing, there are different types of files formats to store your data sets. Each format has its own pros and cons depending upon the use cases and exists to serve one or several purposesā¦
By Aida NGOM
Jul 23, 2020
Hadoop Ozone part 3: advanced replication strategy with Copyset
Categories: Infrastructure | Tags: HDFS, Ozone, Cluster, Kubernetes, Node
Hadoop Ozone provide a way of setting a ReplicationType for every write you make on the cluster. Right now is supported HDFS and Ratis but more advanced replication strategies can be achieved. In thisā¦
Dec 3, 2019
Hadoop Ozone part 2: tutorial and getting started of its features
Categories: Infrastructure | Tags: CLI, Learning and tutorial, HDFS, Ozone, Amazon S3, Cluster, REST
The releases of Hadoop Ozone come with a handy docker-compose file to try out Ozone. The below instructions provide details on how to use it. You can also use the Katacoda training sandbox whichā¦
Dec 3, 2019
Hadoop Ozone part 1: an introduction of the new filesystem
Categories: Infrastructure | Tags: HDFS, Ozone, Cluster, Kubernetes
Hadoop Ozone is an object store for Hadoop. It is designed to scale to billions of objects of varying sizes. It is currently in development. The roadmap is available on the project wiki. This articleā¦
Dec 3, 2019
Multihoming on Hadoop
Categories: Infrastructure | Tags: Hadoop, HDFS, Kerberos, Network
Multihoming, which means having multiple networks attached to one node, is one of the main components to manage the heterogeneous network usage of an Apache Hadoop cluster. This article is anā¦
Mar 5, 2019
Deploying a secured Flink cluster on Kubernetes
Categories: Big Data | Tags: Encryption, Flink, HDFS, Kafka, Elasticsearch, Kerberos, SSL/TLS
When deploying secured Flink applications inside Kubernetes, you are faced with two choices. Assuming your Kubernetes is secure, you may rely on the underlying platform or rely on Flink nativeā¦
By David WORMS
Oct 8, 2018
Clusters and workloads migration from Hadoop 2 to Hadoop 3
Categories: Big Data, Infrastructure | Tags: Slider, Erasure Coding, Rolling Upgrade, HDFS, Spark, YARN, Docker
Hadoop 2 to Hadoop 3 migration is a hot subject. How to upgrade your clusters, which features present in the new release may solve current problems and bring new opportunities, how are your currentā¦
Jul 25, 2018
Apache Hadoop YARN 3.0 ā State of the union
Categories: Big Data, DataWorks Summit 2018 | Tags: GPU, Hortonworks, Hadoop, HDFS, MapReduce, YARN, Cloudera, Data Science, Docker, Release and features
This article covers the āApache Hadoop YARN: state of the unionā talk held by Wangda Tan from Hortonworks during the Dataworks Summit 2018. What is Apache YARN? As a reminder, YARN is one of the twoā¦
May 31, 2018
Apache Metron in the Real World
Categories: Cyber Security, DataWorks Summit 2018 | Tags: Algorithm, NiFi, Solr, Storm, pcap, RDBMS, HDFS, Kafka, Metron, Spark, Data Science, Elasticsearch, SQL
Apache Metron is a storage and analytic platform specialized in cyber security. This talk was about demonstrating the usages and capabilities of Apache Metron in the real world. The presentation wasā¦
May 29, 2018
Red Hat Storage Gluster and its integration with Hadoop
Categories: Big Data | Tags: GlusterFS, Red Hat, Hadoop, HDFS, Storage
I had the opportunity to be introduced to Red Hat Storage and Gluster in a joint presentation by Red Hat France and the company StartX. I have here recompiled my notes, at least partially. I willā¦
By David WORMS
Jul 3, 2015
Splitting HDFS files into multiple hive tables
Categories: Data Engineering | Tags: Flume, Pig, HDFS, Hive, Oozie, SQL
I am going to show how to split a CSV file stored inside HDFS as multiple Hive tables based on the content of each record. The context is simple. We are using Flume to collect logs from all over ourā¦
By David WORMS
Sep 15, 2013
Kerberos and delegation tokens security with WebHDFS
Categories: Cyber Security | Tags: HTTP, HDFS, Big Data, Kerberos
WebHDFS is an HTTP Rest server bundle with the latest version of Hadoop. What interests me on this article is to dig into security with the Kerberos and delegation tokens functionalities. I will coverā¦
By David WORMS
Jul 25, 2013
Testing the Oracle SQL Connector for Hadoop HDFS
Categories: Data Engineering | Tags: Database, File system, Oracle, HDFS, CDH, SQL
Using Oracle SQL Connector for HDFS, you can use Oracle Database to access and analyze data residing in HDFS files or a Hive table. You can also query and join data in HDFS or a Hive table with otherā¦
By David WORMS
Jul 15, 2013
Oracle to Apache Hive with the Oracle SQL Connector
Categories: Business Intelligence | Tags: Oracle, HDFS, Hive, Network
In a previous article published last week, I introduced the choices available to connect Oracle and Hadoop. In a follow up article, I covered the Oracle SQL Connector, its installation and integrationā¦
By David WORMS
May 27, 2013
Options to connect and integrate Hadoop with Oracle
Categories: Data Engineering | Tags: Database, Java, Oracle, R, RDBMS, Avro, HDFS, Hive, MapReduce, Sqoop, NoSQL, SQL
I will list the different tools and libraries available to us developers in order to integrate Oracle and Hadoop. The Oracle SQL Connector for HDFS described below is covered in a follow up articleā¦
By David WORMS
May 15, 2013
Merging multiple files in Hadoop
Categories: Hack | Tags: File system, Hadoop, HDFS
This is a command I used to concatenate the files stored in Hadoop HDFS matching a globing expression into a single file. It uses the āgetmergeā utility of but contrary to āgetmergeā, the finalā¦
By David WORMS
Jan 12, 2013
Hadoop and R with RHadoop
Categories: Business Intelligence, Data Science | Tags: Thrift, Learning and tutorial, R, Hadoop, HBase, HDFS, MapReduce, Data Analytics
RHadoop is a bridge between R, a language and environment to statistically explore data sets, and Hadoop, a framework that allows for the distributed processing of large data sets across clusters ofā¦
By David WORMS
Jul 19, 2012
HDFS and Hive storage - comparing file formats and compression methods
Categories: Big Data | Tags: Business intelligence, Hive, ORC, Parquet, File Format
A few days ago, we have conducted a test in order to compare various Hive file formats and compression methods. Among those file formats, some are native to HDFS and apply to all Hadoop users. Theā¦
By David WORMS
Mar 13, 2012
Two Hive UDAF to convert an aggregation to a map
Categories: Data Engineering | Tags: Java, HBase, Hive, File Format
I am publishing two new Hive UDAF to help with maps in Apache Hive. The source code is available on GitHub in two Java classes: āUDAFToMapā and āUDAFToOrderedMapā or you can download the jar file. Theā¦
By David WORMS
Mar 6, 2012
Timeseries storage in Hadoop and Hive
Categories: Data Engineering | Tags: CRM, timeseries, Tuning, Hadoop, HDFS, Hive, File Format
In the next few weeks, we will be exploring the storage and analytic of a large generated dataset. This dataset is composed of CRM tables associated to one timeserie table of about 7,000 billiard rowsā¦
By David WORMS
Jan 10, 2012
Storage and massive processing with Hadoop
Categories: Big Data | Tags: Hadoop, HDFS, Storage
Apache Hadoop is a system for building shared storage and processing infrastructures for large volumes of data (multiple terabytes or petabytes). Hadoop clusters are used by a wide range of projectsā¦
By David WORMS
Nov 26, 2010