Hadoop Monitoring Guide & 3 Best Hadoop Monitoring Tools (Paid & Free) (2023)

In an era where enterprises are looking to leverage the power of big data, Apache Hadoop has become a key tool for storing and processing large datasets efficiently. However, like all pieces of infrastructure, it needs to be monitored.

Here is our list of the best Hadoop monitoring tools:

  1. Datadog EDITOR’S CHOICE – Cloud monitoring software with a customizable Hadoop dashboard, integrations, alerts, and more.
  2. LogicMonitor – Infrastructure monitoring software with a HadoopPackage, REST API, alerts, reports, dashboards, and more.
  3. Dynatrace – Application performance management software with Hadoop monitoring with NameNode/DataNode metrics, dashboards, analytics,, custom alerts, and more.

What is Apache Hadoop?

Apache Hadoop is an open-source software framework that can process and distribute large data sets across multiple clusters of computers. Hadoop was designed to break down data management workloads over a cluster of computers. It divides data processing between multiple nodes, which manages the datasets more efficiently than a single device could.

How to Monitor Hadoop: Metrics You Need to Keep Track of to Monitor Hadoop Clusters

Like any computing resource, Hadoop clusters need to be monitored to ensure that they keep performing at their best. Hadoop’s architecture may be resilient to system failures, but it still needs maintenance to prevent jobs from being disrupted. When monitoring the status of clusters, there are four main categories of metrics you need to be aware of:

  • HDFS metrics (NameNode metrics and DataNode metrics)
  • MapReduce counters
  • YARN metrics
  • ZooKeeper metrics

Below, we’re going to break each of these metric types down, explaining what they are and providing a brief guide for how you can monitor them.

HDFS Metrics

Apache Hadoop Distributed File System (HDFS) is a distributed file system with a NameNode and DataNode architecture. Whenever the HDFS receives data it breaks it down into blocks and sends it to multiple nodes. The HDFS is scalable and can support thousands of nodes.

Monitoring key HDFS metrics is important because it helps you to: monitor the capacity of the DFS, monitor the space available, track the status of blocks, and optimize the storage of your data.

There are two main categories of HDFS metrics:

  • NameNode metrics
  • DataNode metrics

NameNodes and DataNodes

HDFS follows a master-slave architecture where every cluster in the HDFS is composed of a single NameNode (master) and multiple DataNodes (slave). The NameNode controls access to files, records metadata of files stored in the cluster, and monitors the state of DataNodes.

A DataNode is a process that runs on each slave machine, which performs low-level read/write requests from the system’s clients, and sends periodic heartbeats to the NameNode, to report on the health of the HDFS. The NameNode then uses the health information to monitor the status of DataNodes and verify that they’re live.

When monitoring, it’s important to prioritize analyzing metrics taken from the NameNode because if a NameNode fails, all the data within a cluster will become inaccessible to the user.

Prioritizing monitoring NameNode also makes sense as it enables you to ascertain the health of all the data nodes within a cluster. NameNode metrics can be broken down into two groups:

  • NameNode-emitted metrics
  • NameNode Java Virtual Machine (JVM) metrics

Below we’re going to list each group of metrics you can monitor and then show you a way to monitor these metrics for HDFS.

NameNode-emitted metrics

  • CapacityRemaining – Records the available capacity
  • CorruptBlocks/MissingBlocks – Records number of corrupt/missing blocks
  • VolumeFailuresTotal – Records number of failed volumes
  • NumLiveDataNodes/NumDeadDataNodes – Records count of alive or dead DataNodes
  • FilesTotal – Total count of files tracked by the NameNode
  • Total Load – Measure of file access across all DataNodes
  • BlockCapacity/BlocksTotal – Maximum number of blocks allocable/count of blocks tracked by NameNode
  • UnderReplicated Blocks – Number of under-replicated blocks
  • NumStaleDataNodes – Number of stale DataNodes

NameNode JVM Metrics

  • ConcurrentMarkSweep count – Number of old-generation collections
  • ConcurrentMarkSweep time – The elapsed time of old-generation collections, in milliseconds

How to Monitor HDFS Metrics

One way that you can monitor HDFS metrics is through Java Management Extensions (JMX) and the HDFS daemon web interface. To view a summary of NameNode and performance metrics enter the following URL into your web browser to access the web interface (which is available by default at port 50070):


Here you’ll be able to see information on Configured Capacity, DFS Used, Non-DFS Used, DFS Remaining, Block Pool Used, DataNodes usage%, and more.

If you require more in-depth information, you can enter the following URL to view more metrics with a JSON output:


MapReduce Counters

MapReduce is a software framework used by Hadoop to process large datasets in-parallel across thousands of nodes. The framework breaks down a dataset into chunks and stores them in a file system. MapReduce jobs are responsible for splitting the datasets and map tasks then process the data.

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For performance monitoring purposes, you need to monitor MapReduce counters, so that you view information/statistics about job execution. Monitoring MapReduce counters enables you to monitor the number of rows read, and the number of rows written as output.

You can use MapReduce counters to find performance bottlenecks. There are two main types of MapReduce counters:

  • Built-in Counters – Counters that are included with MapReduce by default
  • Custom counters – User-defined counters that the user can create with custom code

Below we’re going to look at some of the built-in counters you can use to monitor Hadoop.

Built-In MapReduce Counters

Built-in Counters are counters that come with MapReduce by default. There are five main types of built-in counters:

  • Job counters
  • Task counters
  • File system counters
  • FileInputFormat Counters
  • FileOutput Format Counters

Job Counters

MapReduce job counters measure statistics at the job level, such as the number of failed maps or reduces.

  • MILLIS_MAPS/MILLIS_REDUCES – Processing time for maps/reduces
  • NUM_FAILED_MAPS/NUM_FAILED_REDUCES – Number of failed maps/reduces
  • RACK_LOCAL_MAPS/DATA_LOCAL_MAPS/OTHER_LOCAL_MAPS – Counters tracking where map tasks were executed

Task Counters

Task counters collect information about tasks during execution, such as the number of input records for reduce tasks.

  • REDUCE_INPUT_RECORDS – Number of input records for reduce tasks
  • SPILLED_RECORDS – Number of records spilled to disk
  • GC_TIME_MILLIS – Processing time spent in garbage collection

FileSystem Counters

FileSystem Counters record information about the file system, such as the number of bytes read by the FileSystem.

  • FileSystem bytes read – The number of bytes read by the FileSystem
  • FileSystem bytes written – The number of bytes written to the FileSystem

FIleInputFormat Counters

FileInputFormat Counters record information about the number of bytes read by map tasks

  • Bytes read – Displays the bytes read by map tasks with the specific input format

File OutputFormat Counters

FileOutputFormat counters gather information on the number of bytes written by map tasks or reduce tasks in the output format.

  • Bytes written – Displays the bytes written by map and reduce tasks with the specified format

How to Monitor MapReduce Counters

You can monitor MapReduce counters for jobs through the ResourceManager web UI. To load up the ResourceManager web UI, go to your browser and enter the following URL:


Here you will be shown a list of All Applications in a table format. Now, go to the application you want to monitor and click the History hyperlink in the Tracking UI column.

On the application page, click on the Counters option on the left-hand side. You will now be able to view counters associated with the job monitored.

YARN Metrics

Yet Another Resource Negotiator (YARN) is the component of Hadoop that’s responsible for allocating system resources to the applications or tasks running within a Hadoop cluster.

There are three main categories of YARN metrics:

  • Cluster metrics – Enable you to monitor high-level YARN application execution
  • Application metrics -Monitor execution of individual YARN applications
  • NodeManager metrics – Monitor information at the individual node level

Cluster Metrics

Cluster metrics can be used to view a YARN application execution.

  • unhealthyNodes – Number of unhealthy nodes
  • activeNodes – Number of currently active nodes
  • lostNodes – Number of lost nodes
  • appsFailed – Number of failed applications
  • totalMB/allocatedMB – Total amount of memory/amount of memory allocated

Application metrics

Application metrics provide in-depth information on the execution of YARN applications.

  • progress – Application execution progress meter

NodeManager metrics

NodeManager metrics display information on resources within individual nodes.

  • containersFailed – Number of containers that failed to launch

How to Monitor YARN Metrics

To collect metrics for YARN, you can use the HTTP API. With your resource manager, host query the yarn metrics located on port 8088 by entering the following (use the qry parameter to specify the MBeans you want to monitor).

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ZooKeeper Metrics

ZooKeeper is a centralized service that maintains configuration information and delivers distributed synchronization across a Hadoop cluster. ZooKeeper is responsible for maintaining the availability of the HDFS NameNode and YARNs ResourceManager.

Some key ZooKeeper metrics you should monitor include:

  • zk_followers – Number of active followers
  • zk_avg_latency – Amount of time it takes to respond to a client request (in ms)
  • zk_num_alive_connections – Number of clients connected to ZooKeeper

How to Collect Zookeeper Metrics

There are a number of ways you can collect metrics for Zookeeper, but the easiest is by using the 4 letter word commands through Telnet or Netcat at the client port. To keep things simple, we’re going to look at the mntr, arguably the most important of the four 4 letter word commands.

$ echo mntr | nc localhost 2555

Entering the mntr command will return you information on average latency, maximum latency, packets received, packets sent, outstanding requests, number of followers, and more. You can view a list of four-letter word commands on the Apache ZooKeeper site.

Hadoop Monitoring Software

Monitoring Hadoop metrics through JMX or an HTTP API enables you to see the key metrics, but it isn’t the most efficient method of monitoring performance. The most efficient way to collect and analyze HDFS, MapReduce, Yarn, and ZooKeeper metrics, is to use an infrastructure monitoring tool or Hadoop monitoring software.

Many network monitoring providers have designed platforms with the capacity to monitor frameworks like Hadoop, with state-of-the-art dashboards and analytics to help the user monitor the performance of clusters at a glance. Many also come with custom alerts systems that provide you with email and SMS notifications when a metric hits a problematic threshold.

In this section, we’re going to look at some of the top Hadoop monitoring tools on the market. We’ve prioritized tools with high-quality visibility, configurable alerts systems, and complete data visualizations.

Our methodology for selecting Hadoop monitoring tools 

We reviewed the market for Hadoop monitors and analyzed tools based on the following criteria:

  • A counter to record the log message throughput rate
  • Alerts for irregular log throughput rates
  • Throughput of Hadoop-collected system statistics
  • Collection of HDFS, MapReduce, Yarn, and ZooKeeper metrics
  • Pre-written searches to make sense of Hadoop data
  • A free tool or a demo package for a no-obligation assessment
  • Value for money offered by a thorough Hadoop data collection tool that is provided at a fair price

With these selection criteria in mind, we selected a range of tools that both monitor Hadoop activities and pass through the data collected by Hadoop on disk and data management activity.

1. Datadog

Datadog is a cloud monitoring tool that can monitor services and applications. With Datadog you can monitor the health and performance of Apache Hadoop. There is a Hadoop dashboard that displays information on DataNodes and NameNodes.

For example, you can view a graph of Disk remaining by DataNode, and TotalLoad by NameNode. Dashboards can be customized to add information from other systems as well. Integrations for HDFS, MapReduce, YARN, and ZooKeeper enable you to monitor the most significant performance indicators.

Key features:

  • Hadoop monitoring dashboard
  • Integrations for HDFS, MapReduce, YARN, and ZooKeeper
  • Alerts
  • Full API access

The alerts system makes it easy for you to track performance changes when they occur by providing you with automatic notifications. For example, Datadog can notify you if Hadoop jobs fail. The alerts system uses machine learning, which has the ability to identify anomalous behavior.

To give you greater control over your monitoring experience, Datadog provides full API access so that you can create new integrations. You can use the API access to complete tasks such as querying Datadog in the command-line or creating JSON-formatted dashboards.

Datadog is a great starting point for enterprises that want comprehensive Hadoop monitoring with wider cloud monitoring capabilities. The Infrastructure package of Datadog starts at $15 (£11.47) per host, per month. You can start the free trial version via this link here.


  • Offers templates and prebuilt monitors for Hadoop monitoring and security enforcement
  • Easy to use customizable dashboards
  • Supports auto-discovery that builds network topology maps on the fly
  • Changes made to the network are reflected in near real-time
  • Allows businesses to scale their monitoring efforts reliably through flexible pricing options


  • Would like to see a longer trial period for testing
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Datadog is our top pick for a Hadoop monitoring tool because it is able to interface directly to the Hadoop platform and extract the activity metrics that are recorded by the system. The Datadog package is a platform of many tools and there is also a log collection and management service in there, so, not only can you process the data that Hadoop collects on its activities but also store the status messages created by the service and identify unusual variations in activity.

Download: Get a 14-day free trial

Official Site: https://www.datadoghq.com/free-datadog-trial/

OS: Cloud based

2. LogicMonitor

LogicMonitor is an infrastructure monitoring platform that can be used for monitoring Apache Hadoop. LogicMonitor comes with a Hadoop package that can monitor HDFS NameNode, HDFS DataNode, Yarn, and MapReduce metrics. For monitoring Hadoop all you need to do is add Hadoop hosts to monitor, enable JMX on the Hadoop hosts, and assign properties to each resource. The tool then collects Hadoop metrics through a REST API.

Key features:

  • Monitors HDFS NameNode, HDFS DataNode, Yarn, and MapReduce metrics
  • Custom alert thresholds
  • Dashboard
  • Reports

To monitor these metrics you can set alert trigger conditions to determine when alerts will be raised. Alerts can be assigned a numeric priority value to determine the severity of a breach. There is also an escalation chain you can use to escalate alerts that haven’t been responded to.

For more general monitoring activity, LogicMonitor includes a dashboard that you can use to monitor your environment with key metrics and visualizations including graphs and charts. The software also allows you to schedule reports to display performance data. For example, the Alert Trends report provides a summary of alerts that occurred for resources/groups over a period of time.

LogicMonitor is ideal for enterprises that want to monitor Apache Hadoop alongside other applications and services. The tool has a custom pricing model so you need to contact the company directly to request a quote. You can start the free trial version via this link here.


  • Includes Hadoop monitoring and tailored dashboards
  • Monitors application performance via the cloud
  • Can monitor assets in hybrid cloud environments
  • The dashboard can be customized and saved, great for different NOC teams or individual users


  • The trial is only 14 days, would like to see a longer testing period

3. Dynatrace

Dynatrace is an application performance management tool you can use to monitor services and applications. Dynatrace also offers users performance monitoring for Hadoop. The platform can automatically detect Hadoop components and display performance metrics for HDFS and MapReduce. Whenever a new host running Hadoop is added to your environment the tool detects it automatically.

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Key features:

  • Automatically detects Hadoop components
  • DataNode and NameNode metrics
  • Analytics and Data visualizations
  • Dashboards
  • Custom alerts

You can monitor a range of NameNode and DataNode metrics. NameNode metrics include Total, Used, Remaining, Total load, Total, Pending deletion, Files total, Under replicated, Live, Capacity, and more. Types of DataNode metrics include Capacity, Used, Cached, Failed to Cache, Blocks, Removed, Replicated, and more.

Dashboards provide a range of information with rich data visualizations. For example, you can view a chart of MapReduce maps failed or a bar graph of Jobs preparing and running. Custom alerts powered by anomaly detection enable you to identify performance issues, helping you to make sure that your service stays available.

Dynatrace is not only a top application monitoring tool but a formidable choice for monitoring Hadoop as well. There are a range of packages available including the Infrastructure monitoring package at $2 (£1.53) per month and the Full-stack monitoring package at $69 (£52.78) per month. You can start the 15-day free trial via this link here.


  • Offers support for Hadoop environments including templates dashboards
  • Highly visual and customizable dashboards, excellent for enterprise NOCs
  • Operates in the cloud, allowing it to be platform-independent
  • Can monitor application uptime as well as the supporting infrastructure and user experience


  • Designed specifically for large networks, smaller organizations may find the product overwhelming

Choosing Hadoop Monitoring Software for Cluster Performance

Monitoring Hadoop metrics is vital for making sure that your clusters stay up and running. While you can attempt to monitor Hadoop metrics through JMX or an HTTP API, it doesn’t offer the complete monitoring experience that many infrastructure monitoring tools like Datadog, LogicMonitor, and Dynatrace do.

These tools offer features like custom dashboards and alerts that provide you with a more holistic perspective of what’s going on. By collecting all of your Hadoop performance data and putting it in one place, you’ll be able to monitor the performance of your systems much more effectively.

Hadoop monitoring FAQs

What are yarn metrics?

YARN stands for Yet Another Resource Negotiator. It is a component of Hadoop and it allocates system resources to processes running within Hadoop clusters. There are three main categories of YARN metrics:

  • Cluster metrics
  • Application metrics
  • NodeManager metrics

What is JMX in Hadoop?

JMX stands for Java Management Extensions. It is a framework that supports monitoring for systems that are built with Java. Hadoop is written in Java and so its metrics collection occurs through JMX.

What are the main component of Hadoop?

Hadoop has three components:

  • Hadoop HDFS – Hadoop Distributed File System, which is Hadoop’s storage manager
  • Hadoop MapReduce – Hadoop’s processing unit
  • Hadoop YARN – Hadoop’s resource management unit


What is Hadoop monitoring? ›

Hadoop monitoring is a Gateway configuration file that enables monitoring of the Hadoop cluster, nodes, and daemons through the JMX and Toolkit plug-ins. This Hadoop integration template consists of the following components: Hadoop Distributed File System (HDFS) Yet Another Resource Negotiator (YARN)

Which are the three types of data in Hadoop? ›

Hadoop's HDFS can store different data formats, like structured, semi-structured, and unstructured.

Which tool is used for managing and monitoring health of Hadoop cluster? ›

Datadog. Datadog is a cloud monitoring tool that can monitor services and applications. With Datadog you can monitor the health and performance of Apache Hadoop. There is a Hadoop dashboard that displays information on DataNodes and NameNodes.

How do I monitor Hadoop metrics? ›

Go to Server > Hadoop > click on the Hadoop cluster > NameNodes/DataNodes/YARN > click on the monitor. Ensure the Site24x7 Linux Monitoring agent is installed in every DataNode, NameNode, and YARN to view the following performance metrics.

What are the 2 main components of Hadoop? ›

HDFS (storage) and YARN (processing) are the two core components of Apache Hadoop.
Hadoop Distributed File System (HDFS)
  • NameNode is the master of the system. ...
  • DataNodes are the slaves which are deployed on each machine and provide the actual storage. ...
  • Secondary NameNode is responsible for performing periodic checkpoints.

Which tool is used for Hadoop? ›

Apache Sqoop

It is a command-line interface, mostly used to move data between Hadoop and structured data stores or mainframes. It imports data from RDBMS and stores it in HDFS, transformed it into MapReduce, and is sent back to RDBMS. It comes with a data export tool and a primitive execution shell.

What are the 4 main components of Hadoop? ›

There are various components within the Hadoop ecosystem such as Apache Hive, Pig, Sqoop, and ZooKeeper. Various tasks of each of these components are different. Hive is an SQL dialect that is primarily used for data summarization, querying, and analysis.

What are the 3 major components of big data? ›

There are four major components of big data.
  • Volume. Volume refers to how much data is actually collected. ...
  • Veracity. Veracity relates to how reliable data is. ...
  • Velocity. Velocity in big data refers to how fast data can be generated, gathered and analyzed. ...
  • Variety.

What are the 4 main data types? ›

The data is classified into majorly four categories:
  • Nominal data.
  • Ordinal data.
  • Discrete data.
  • Continuous data.

Which is the most common tool used for network monitoring? ›

SNMP: The Simple Network Management Protocol, a.k.a. SNMP, is one of the most common network monitoring protocols. SNMP can be used for polling (a monitoring station queries a network device) and notifications (a device sends an SNMP TRAP or INFORM to a monitoring station).

Which tool is used for monitoring? ›

Atera is a monitoring tool available as one of two SaaS systems: professional services automation (PSA) and remote monitoring and management (RMM). Managed service providers and IT teams use the agent-based Atera solution to monitor unlimited hardware and software endpoints and troubleshoot system issues.

Which tool is used for continuous monitoring? ›

Nagios. Nagios is one of the DevOps tools for continuous monitoring. It is a widely-used open-source tool. In a DevOps culture, Nagios can assist to monitor systems, applications, services, and business processes.

What would the top three metrics to monitor? ›

The 3 Most Important Online Marketing Metrics to Monitor
  • Conversion rate and goal completion. If you're investing time and money in building a site and expanding your web presence, be sure you're getting something of value in return. ...
  • Backlink profile. ...
  • Visitor engagement.
Aug 29, 2012

How do you validate data in Hadoop? ›

Data Validation

For these steps, the tools used are : a) Datameer, b) Talent and c) Informatica. Simply, it ensures only the right data enter the Hadoop Distributed File System (HDFS) location. After this step is complete, the data is moved to the next stage of Hadoop testing system.

How can I check my Hadoop data? ›

You can use the Hadoop filesystem command to read any file. It supports the cat command to read the content.

What are the 3 main parts of the Hadoop infrastructure? ›

Hadoop has three core components, plus ZooKeeper if you want to enable high availability:
  • Hadoop Distributed File System (HDFS)
  • MapReduce.
  • Yet Another Resource Negotiator (YARN)
  • ZooKeeper.
Dec 18, 2020

How many tools are there in Hadoop? ›

The 3 core components of Hadoop are Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Source Negotiator (YARN).

What are three features of Hadoop? ›

Features of Hadoop
  • Hadoop is Open Source. ...
  • Hadoop cluster is Highly Scalable. ...
  • Hadoop provides Fault Tolerance. ...
  • Hadoop provides High Availability. ...
  • Hadoop is very Cost-Effective. ...
  • Hadoop is Faster in Data Processing. ...
  • Hadoop is based on Data Locality concept. ...
  • Hadoop provides Feasibility.

Which language is used in Hadoop? ›

The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts.

Is Hadoop a Python or Java? ›

Hadoop framework is written in Java language; however, Hadoop programs can be coded in Python or C++ language.

Which database is used in Hadoop? ›

HBase – An open source, non-relational, versioned database that runs on top of Amazon S3 (using EMRFS) or the Hadoop Distributed File System (HDFS). HBase is a massively scalable, distributed big data store built for random, strictly consistent, real-time access for tables with billions of rows and millions of columns.

What are the four key pillars of Hadoop security? ›

In a traditional on-premises Hadoop environment, the four pillars of Hadoop security (authentication, authorization, encryption, and audit) are integrated and handled by different components.

What is the difference between Hadoop and HDFS? ›

Hadoop itself is an open source distributed processing framework that manages data processing and storage for big data applications. HDFS is a key part of the many Hadoop ecosystem technologies. It provides a reliable means for managing pools of big data and supporting related big data analytics applications.

What is a MapReduce in Hadoop? ›

MapReduce is a programming paradigm that enables massive scalability across hundreds or thousands of servers in a Hadoop cluster. As the processing component, MapReduce is the heart of Apache Hadoop. The term "MapReduce" refers to two separate and distinct tasks that Hadoop programs perform.

What are at least 3 sources of big data? ›

The Primary Sources of Big Data:
  • Machine Data. In-Demand Software Development Skills.
  • Social Data.
  • Transactional Data.
Sep 27, 2021

What are the three basic data? ›

Most programming languages support basic data types of integer numbers (of varying sizes), floating-point numbers (which approximate real numbers), characters and Booleans.

What are the 2 main types of data? ›

There are two general types of data – quantitative and qualitative and both are equally important. You use both types to demonstrate effectiveness, importance or value.

What are the 5 main data types? ›

Most modern computer languages recognize five basic categories of data types: Integral, Floating Point, Character, Character String, and composite types, with various specific subtypes defined within each broad category.

What are the 7 types of data? ›

And there you have the 7 Data Types.
  • Useless.
  • Nominal.
  • Binary.
  • Ordinal.
  • Count.
  • Time.
  • Interval.
Aug 29, 2018

Which are the three basic tools for monitoring? ›

Monitoring tools allow you to track progress, identify challenges, and assess the implementation of a program or project. These tools can include key performance indicators (KPIs), dashboards, checklists, and monitoring plans.

What is the best way to monitor your network? ›

20 Ways to Monitor Network Traffic
  1. Install a packet sniffer like Wireshark on your computer. ...
  2. Use NetFlow or sflow data from your routers and switches. ...
  3. Use the built-in tools in your operating system to monitor network traffic. ...
  4. Use third-party software to monitor network traffic.
Jun 15, 2022

What is the best monitoring? ›

Top 10 Enterprise Monitoring Software
  • Dynatrace.
  • LogicMonitor.
  • Datadog.
  • Splunk Enterprise.
  • Pandora FMS.
  • Checkmk.
  • Mezmo Log Analysis.
  • PRTG.

What are seven monitoring tools? ›

Recognizing the need is easy, but choosing which monitoring tool or set of tools to use can be difficult. The seven tools I wrote about here – Datadog, Ruxit, OverOps, Rollbar, Sensu, ELK Stack, and Graphite – are worthwhile tools to check out.

What are the different types of monitoring systems? ›

Types of IT monitoring
  • System monitoring. ...
  • Dependency monitoring. ...
  • Integration and API monitoring. ...
  • Business Activity Monitoring (BAM) ...
  • Web performance monitoring. ...
  • Application Performance Monitoring (APM) ...
  • Real User Monitoring (RUM) ...
  • Security monitoring.
May 27, 2020

What are smart monitoring tools? ›

S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) is a monitoring system for computer hard disk drives that provides for the detection and reporting of various indicators of reliability. It is often used for early detection of potential future failures.

Which is a key feature of a monitoring tool? ›

Intelligent alerts

Your monitoring tool needs to deliver intelligent real-time alerts on issues that it finds and notify the right network engineer or administrator. Alerts need to provide the right information, including location and severity, and prioritize more crucial problems.

What are the three types of metrics? ›

There are three types of metrics:
  • Technology metrics – component and application metrics (e.g. performance, availability…)
  • Process metrics – defined, i.e. measured by CSFs and KPIs.
  • Service metrics – measure of end-to-end service performance.
Apr 2, 2013

What are the 5 main metrics used in daily management? ›

5 key business metrics you should track to measure performance
  • Sales Revenue. Tracking sales revenue helps you measure your financial performance. ...
  • Customer Acquisition Costs. Customer Acquisition Costs are the expenses related to acquiring new customers. ...
  • Customer Churn. ...
  • Customer Engagement. ...
  • Customer Satisfaction.
May 6, 2020

What are the three A's of metrics? ›

Test your existing metrics against the three “A”s of good metrics; actionable, accessible and auditable.

How many types of testing are there in Hadoop? ›

Hadoop has various kinds of testing like Unit Testing, Regression Testing, System Testing, and Performance Testing, etc.

What are two ways to validate data? ›

Common types of data validation checks include:
  • Data Type Check. A data type check confirms that the data entered has the correct data type. ...
  • Code Check. A code check ensures that a field is selected from a valid list of values or follows certain formatting rules. ...
  • Range Check. ...
  • Format Check. ...
  • Consistency Check. ...
  • Uniqueness Check.
Mar 6, 2023

What is the first step in big data testing? ›

The General approach to test a Big Data Application involves the following stages. Data is first loaded from source to Big Data System using extracting tools. The Storage might be HDFS, MongoDB or any similar storage. Then, the loaded data is cross-checked for errors and missing values.

What is an example of Hadoop? ›

Examples of Hadoop

Financial services companies use analytics to assess risk, build investment models, and create trading algorithms; Hadoop has been used to help build and run those applications. Retailers use it to help analyze structured and unstructured data to better understand and serve their customers.

What does Hadoop command do? ›

It allows the user to append the content of one or many files into a single file on the specified destination file in the HDFS filesystem cluster. On execution of this command, the given source files are appended into the destination source as per the given filename in the command.

What is Hadoop big data testing? ›

Big Data Testing can be defined as the procedure that involves examining and validating the functionality of the Big Data Applications. Big Data is a collection of a huge amount of data that traditional storage systems cannot handle.

What is data monitoring tool? ›

Data monitoring allows an organization to proactively maintain a high, consistent standard of data quality. By checking data routinely as it is stored within applications, organizations can avoid the resource-intensive pre-processing of data before it is moved.

Does Hadoop use SQL? ›

Hadoop uses appropriate Java Database Connectivity (JDBC) to interact with SQL systems to transfer and receive data between them. SQL systems can read and write data to Hadoop systems. Hadoop supports advanced machine learning and artificial intelligence techniques.

What is Hadoop not good for? ›

Multiple Smaller Datasets. Hadoop framework is not recommended for small-structured datasets as you have other tools available in market which can do this work quite easily and at a fast pace than Hadoop like MS Excel, RDBMS etc. For a small data analytics, Hadoop can be costlier than other tools.

How to load data in Hadoop? ›

Inserting Data into HDFS
  1. You have to create an input directory. $ $HADOOP_HOME/bin/hadoop fs -mkdir /user/input.
  2. Transfer and store a data file from local systems to the Hadoop file system using the put command. $ $HADOOP_HOME/bin/hadoop fs -put /home/file.txt /user/input.
  3. You can verify the file using ls command.

Can we use Hadoop in Windows? ›

OPERATING SYSTEM: You can install Hadoop on Windows or Linux based operating systems.


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