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Browse Presentation Creator Pro Upload Jun 05, 2020 170 likes | 239 Views This presentation gives an overview of the Apache Ranger project. It explains Apache Ranger in terms of it's architecture, security, audit and plugin features. Links for further information and connecting Download Presentation Apache Ranger An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher. Presentation Transcript What Is Apache Ranger ? ● For data security across the Hadoop platform ● A framework to enable, monitor and manage security ● Supports security in – A multi tenant data lake – Hadoop eco system ● Open source / Apache 2.0 license ● Administration of security policies ● Monitoring of user access ● Offers central UI and REST API'sWhat Is Apache Ranger ? ● Manage policies for resource access – File, folder, database, table, column ● Policies for users and groups ● Has audit tracking ● Enables policy analytics ● Offers decentralizing data ownershipRanger Projects ● Which projects does Ranger support ? – Apache Hadoop – Apache Hive – Apache HBase – Apache Storm – Apache Knox – Apache Solr – Apache Kafka – YARN – ATLAS ● No additional OS level process to manageRanger Enforcement ● Ranger enforces policy with Java plugins ●

Apache Hadoop 3.4.1 – Apache Hadoop Compatibility

Big Data Service provides enterprise-grade Hadoop as a service, with end-to-end security, high performance, and ease of management and upgradeability.Big Data Service is an Oracle Cloud Infrastructure service designed for a diverse set of big data use cases and workloads. From short-lived clusters used to tackle specific tasks to long-lived clusters that manage large data lakes, Big Data Service scales to meet an organization's requirements at a low cost and with the highest levels of security. Note The data at rest in Block Volumes used by the Big Data Service service is encrypted by default.Big Data Service includes:An Hadoop stack that includes an installation of Oracle Distribution including Apache Hadoop (ODH). ODH includes Apache Ambari, Apache Hadoop, Apache HBase, Apache Hive, Apache Spark, and other services for working with and securing big data. For a detailed list of what's in ODH, see About Oracle Distribution Including Apache Hadoop (ODH).Oracle Cloud Infrastructure features and resources, including identity management, networking, compute, storage, and monitoring. A REST API for creating and managing clusters. The ability to create clusters of any size, based on native Oracle Cloud Infrastructure shapes. For example, you can create small, short-lived clusters in flexible virtual environments, very large, long-running clusters on dedicated hardware, or any combination between. Optional secure, high availability (HA) clusters. Oracle Cloud SQL integration, for analyzing data across Apache Hadoop, Apache Kafka, NoSQL, and object stores using Oracle SQL query language. Full access to customize what is deployed on your Big Data Service clusters.Big Data Service releases patches that are visible in the OCI Console. These patches must be applied to keep your Big Data Service clusters up to date and supported. See Patching in Big Data Service for more details on the Big Data Service release patch.About Oracle Distribution Including Apache Hadoop (ODH)ODH is built from the ground up, natively integrated into Oracle's data platform. ODH is fully managed, with the same Hadoop components you know and build on today. ODH is available as versions ODH 2.x and ODH 1.x.For more information, see:Big Data Service Release and Patch VersionsODH 2.x Based on Apache Hadoop 3.3.3ODH 1.x Based on Apache Hadoop 3.1 NoteApache Hive supports functions for data masking which may include weak algorithms. For strong encryption algorithm custom functions can be written. For more information see Apache Hive UDF Reference at: hive/languagemanual+udf.See Big Data ServiceAbout Oracle Distribution Including Apache Hadoop (ODH) for details of components

Hadoop – Apache Hadoop 3.5.0-SNAPSHOT - The Apache

What is Hadoop HDFS?HDFS ArchitectureNameNodeSecondary NameNodeDataNodeCheckpoint NodeBackup NodeBlocksFeatures of HDFSReplication Management in HDFS ArchitectureWrite OperationRead OperationAdvantages of HDFS ArchitectureDisadvantages of HDFS ArchitectureConclusionAdditional ResourcesHadoop is an open-source framework for distributed storage and processing. It can be used to store large amounts of data in a reliable, scalable, and inexpensive manner. It was created by Yahoo! in 2005 as a means of storing and processing large datasets. Hadoop provides MapReduce for distributed processing, HDFS for storing data, and YARN for managing compute resources. By using Hadoop, you can process huge amounts of data quickly and efficiently. Hadoop can be used to run enterprise applications such as analytics and data mining. HDFS is the core component of Hadoop. It is a distributed file system that provides capacity and reliability for distributed applications. It stores files across multiple machines, enabling high availability and scalability. HDFS is designed to handle large volumes of data across many servers. It also provides fault tolerance through replication and auto-scalability. As a result, HDFS can serve as a reliable source of storage for your application’s data files while providing optimum performance. HDFS is implemented as a distributed file system with multiple data nodes spread across the cluster to store files.What is Hadoop HDFS?Hadoop is a software framework that enables distributed storage and processing of large data sets. It consists of several open source projects, including HDFS, MapReduce, and Yarn. While Hadoop can be used for different purposes, the two most common are Big Data analytics and NoSQL database management. HDFS stands for “Hadoop Distributed File System” and is a decentralized file system that stores data across multiple computers in a cluster. This makes it ideal for large-scale storage as it distributes the load across multiple machines so there’s less pressure on each individual machine. MapReduce is a programming model that allows users to write code once and execute it across many servers. When combined with HDFS, MapReduce can be used to process massive data sets in parallel by dividing work up into smaller chunks and executing them simultaneously.HDFS is an Open source component of the Apache Software Foundation that manages data. HDFS has scalability, availability, and replication as key features. Name nodes, secondary name nodes, data nodes, checkpoint nodes, backup nodes, and blocks all make up the architecture of HDFS. HDFS is fault-tolerant and is replicated. Files are distributed across the cluster systems using the Name node and Data Nodes. The primary difference between Hadoop and Apache HBase is that Apache HBase is a non-relational database and Apache Hadoop is a non-relational data store.Confused about your next job?In 4 simple steps you can find your personalised career roadmap in Software development for FREEExpand in New Tab HDFS is composed of master-slave architecture, which includes the following elements:NameNodeAll the blocks on DataNodes are handled by NameNode, which is known as the master node. It performs the following functions:Monitor and control all the DataNodes instances.Permits the user to access a file.Stores all of the block records on a DataNode instance.EditLogs are. Download tar.gz (checksum signature) Download src (checksum signature) Documentation. Apache Hadoop, Hadoop, Apache, the Apache feather logo, and the Apache Hadoop

Apache Hadoop 2.10.2 – Apache Hadoop Changelog

On the authorized_keys file, run:sudo chmod 640 ~/.ssh/authorized_keysFinally, you are ready to test SSH configuration:ssh localhostNotes:If you didn’t set a passphrase, you should be logged in automatically.If you set a passphrase, you’ll be prompted to enter it.Step 3: Download the latest stable releaseTo download Apache Hadoop, visit the Apache Hadoop download page. Find the latest stable release (e.g., 3.3.4) and copy the download link.Also, you can download the release using wget command:wget extract the downloaded file:tar -xvzf hadoop-3.3.4.tar.gzTo move the extracted directory, run:sudo mv hadoop-3.3.4 /usr/local/hadoopUse the command below to create a directory for logs:sudo mkdir /usr/local/hadoop/logsNow, you need to change ownership of the Hadoop directory. So, use:sudo chown -R hadoop:hadoop /usr/local/hadoopStep 4: Configure Hadoop Environment VariablesEdit the .bashrc file using the command below:sudo nano ~/.bashrcAdd environment variables to the end of the file by running the following command:export HADOOP_HOME=/usr/local/hadoopexport HADOOP_INSTALL=$HADOOP_HOMEexport HADOOP_MAPRED_HOME=$HADOOP_HOMEexport HADOOP_COMMON_HOME=$HADOOP_HOMEexport HADOOP_HDFS_HOME=$HADOOP_HOMEexport YARN_HOME=$HADOOP_HOMEexport HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/nativeexport PATH=$PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/binexport HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib/native"To save changes and source the .bashrc file, type:source ~/.bashrcWhen you are finished, you are ready for Ubuntu Hadoop setup.Step 5: Configure Hadoop Environment VariablesFirst, edit the hadoop-env.sh file by running the command below:sudo nano $HADOOP_HOME/etc/hadoop/hadoop-env.shNow, you must add the path to Java. If you haven’t already added the JAVA_HOME variable in your .bashrc file, include it here:export JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64export HADOOP_CLASSPATH+=" $HADOOP_HOME/lib/*.jar"Save changes and exit when you are done.Then, change your current working directory to /usr/local/hadoop/lib:cd /usr/local/hadoop/libThe below command lets you download the javax activation file:sudo wget you are finished, you can check the Hadoop version:hadoop versionIf you have passed the steps correctly, you can now configure Hadoop Core Site. To edit the core-site.xml file, run:sudo nano $HADOOP_HOME/etc/hadoop/core-site.xmlAdd the default filesystem URI: fs.default.name hdfs://0.0.0.0:9000 The default file system URI Save changes and exit.Use the following command to create directories for NameNode and DataNode:sudo mkdir -p /home/hadoop/hdfs/{namenode,datanode}Then, change ownership of the directories:sudo chown -R hadoop:hadoop /home/hadoop/hdfsTo change the ownership of the created directory to the hadoop user:sudo chown -R hadoop:hadoop /home/hadoop/hdfsTo edit the hdfs-site.xml file, first run:sudo nano $HADOOP_HOME/etc/hadoop/hdfs-site.xmlThen, paste the following line to set the replication factor: dfs.replication 1 Save changes and exit.At this point, you can configure MapReduce. Run the command below to edit the mapred-site.xml file:sudo nano $HADOOP_HOME/etc/hadoop/mapred-site.xmlTo set the MapReduce framework, paste the following line: mapreduce.framework.name yarn Save changes and exit.To configure YARN, run the command below and edit the yarn-site.xml file:sudo nano $HADOOP_HOME/etc/hadoop/yarn-site.xmlPaste the following to enable the MapReduce shuffle service: yarn.nodemanager.aux-services mapreduce_shuffle Save changes and exit.Format the NameNode by

Apache Hadoop 2.10.2 Apache Hadoop Changelog

AdvertisementApache Hbase is column-oriented distributed datastore. Previously, we have shown installing Apache Accumulo. Accumulo is distributed key/value store, built on Hadoop. In another guide we have shown how to install Apache Cassandra. Cassandra is a column-oriented distributed datastore, inspired by BigTable. We have sown how to install Hadoop on single server instance. We can install Hbase without installing Hadoop.The reason to use Apache HBase instead of conventional Apache Hadoop is mainly to do random reads and writes. When we are using Hadoop we are going to read the whole dataset whenever we want to run a MapReduce job. Hadoop is a distributed file system (HDFS) and MapReduce (a framework for distributed computing). HBase is key-value data store built on top of Hadoop (on top of HDFS).Hadoop comprises of HDFS and Map-Reduce. HDFS is a file system which provides a reliable storage with high fault tolerance using replication by distributing the data across a set of nodes. The Hadoop distributed file system aka HDFS provides multiple jobs for us. It consists of 2 components, NameNode (Where the metadata about the file system is stored.) and datanodes(Where the actual distributed data is stored).Map-Reduce is a set of 2 types of java daemons called Job-Tracker and Task-Tracker. Job-Tracker daemon governs the jobs to be executed, whereas the Task-tracker daemons are the daemons which run on top of the data-nodes in which the data is distributed so that they can compute the program execution logic provided by the user specific to the data within the corresponding data-node.HDFS is the storage component and Map-Reduce is the Execution component. As for the HBase concern, simply we can not connect remotely to HBase without using HDFS because HBase can not create clusters and it has its own local file system. HBase comprises of HMaster (Which consists of

Apache Hadoop 3.4.1 – Apache Hadoop Changelog

Database Service Node to Run the Examples with Oracle SQL Connector for HDFSYou must configure the co-managed Database service node in order to run the examples, as shown below. See Oracle Big Data Connectors User's Guide, section Installing and Configuring a Hadoop Client on the Oracle Database System for more details. Generate Oracle SQL Connector for HDFS zip file on the cluster node and copy to the database node. Example:cd /opt/oraclezip -r /tmp/orahdfs-.zip orahdfs-/*Unzip the Oracle SQL Connector for HDFS zip file on the database node. Example:mkdir -p /u01/misc_products/bdcunzip orahdfs-.zip -d /u01/misc_products/bdcInstall the Hadoop client on the database node in the /u01/misc_products/ directory.Connect as the sysdba user for the PDB and verify that both OSCH_BIN_PATH and OSCH_DEF_DIR database directories exist and point to valid operating system directories. For example, create or replace directory OSCH_BIN_PATH as '/u01/misc_products/bdc/orahdfs-/bin'; grant read,execute on directory OSCH_BIN_PATH to OHSH_EXAMPLES; where OHSH_EXAMPLES is the user created in Step 2: Create the OHSH_EXAMPLES User, above.create or replace directory OSCH_DEF_DIR as '/u01/misc_products/bdc/xtab_dirs'; grant read,write on directory OSCH_DEF_DIR to OHSH_EXAMPLES; Note: create the xtab_dirs operating system directory if it doesn't exist. Change to your OSCH (Oracle SQL Connector for HDFS) installation directory, and edit the configuration file hdfs_stream. For example,sudo su -l oracle cd /u01/misc_products/bdc/orahdfs- vi bin/hdfs_streamCheck that the following variables are configured correctly. Read the instructions included in the hdfs_stream file for more details.#Include Hadoop client bin directory to the PATH variable export PATH=/u01/misc_products/hadoop-/bin:/usr/bin:/bin export JAVA_HOME=/usr/java/jdk #See explanation below export HADOOP_CONF_DIR=/u01/misc_products/hadoop-conf#Activate the Kerberos configuration for secure clustersexport HADOOP_CLIENT_OPTS="-Djava.security.krb5.conf=/u01/misc_products/krb5.conf"Configure the Hadoop configuration directory (HADOOP_CONF_DIR). If it's not already configured, use Apache Ambari to download the Hadoop Client configuration archive file, as follows:Login to Apache Ambari. the HDFS service, and select the action Download Client Configuration. Extract the files under the HADOOP_CONF_DIR (/u01/misc_products/hadoop-conf) directory. Ensure that the hostnames and ports configured in HADOOP_CONF_DIR/core-site.xml are accessible from your co-managed Database service node (see the steps below). For example, fs.defaultFS hdfs://bdsmyhostmn0.bmbdcsxxx.bmbdcs.myvcn.com:8020 In this example host bdsmyhostmn0.bmbdcsxxx.bmbdcs.myvcn.com and port 8020 must be accessible from your co-managed Database service node. For secure clusters:Copy the Kerberos configuration file from the cluster node to the database node. Example:cp krb5.conf /u01/misc_products/Copy the Kerberos keytab file from the cluster node to the database node. Example:cp /u01/misc_products/Run the following commands to verify that HDFS access is working. #Change to the Hadoop client bin directory cd /u01/misc_products/hadoop-/bin #--config points to your HADOOP_CONF_DIR directory. ./hadoop --config /u01/misc_products/hadoop-conf fs -ls This command should list the HDFS contents. If you get a timeout or "no route to host" or "unknown host" errors, you will need to update your /etc/hosts file and verify your Big Data Service Console network configuration, as follows:Sign into the Cloud Console, click Big Data, then Clusters, then your_cluster>, then Cluster Details. Under the List of cluster nodes section, get the fully qualified name of all your cluster nodes and all the IP addresses .Edit your co-managed Database service configuration file /etc/hosts, for example: #BDS hostnames xxx.xxx.xxx.xxx bdsmynodemn0.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodemn0 xxx.xxx.xxx.xxx bdsmynodewn0.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodewn0 xxx.xxx.xxx.xxx bdsmynodewn2.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodewn2 xxx.xxx.xxx.xxx bdsmynodewn1.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodewn1 xxx.xxx.xxx.xxx bdsmynodeun0.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodeun0. Download tar.gz (checksum signature) Download src (checksum signature) Documentation. Apache Hadoop, Hadoop, Apache, the Apache feather logo, and the Apache Hadoop