Top 15 big data tools to explore in 2023
Top 15 big data tools to explore in 2023
Due to the expansion of several data-driven and intelligent tools, such as AI systems and IoT devices, the amount of data available has increased over time. According to experts, we produce roughly 2.5 quintillion bytes of data every day.
Organizations face trouble keeping up with their data and figuring out how to store it efficiently. For data to be useful, it must be put to use, and that requires effort. It necessitates a significant amount of time to create clean data, which would be useful to the client and structured to allow for insightful analysis. This is where Big Data tools play their part. Big Data tools help extract and manage huge data efficiently from a large number of data sets and help process this complex data in a structured format.
This article will help you understand Big Data and the significant factors to keep in mind while selecting Big Data Tools. You will also discover some of the popular Big Data tools in the market.
What is big data?
Big Data is a collection of data that is enormous in volume and is expanding exponentially over time. This data may be presented in an unstructured, semi-structured, or structured format. No typical data management systems can effectively store or process this data because of its complexity and volume.
Big Data has many definitions, but most of them centre on the idea of the "5 V's". Let’s explore these below:
- Volume: It is crucial to take the volume of data into account. There will be a lot of low-density, unstructured data that you must analyze. For certain firms, the amount of Big Data might range from tens of gigabytes to hundreds of petabytes.
- Variety: Variety signifies the availability of many forms of data. Traditional data formats had a clear structure and were simple to put into a relational database. New types of unstructured data have evolved as a result of the expansion of big data. These unstructured and semi-structured data formats, such as text, audio, and video, require further preprocessing in order to infer meaning and provide metadata.
- Velocity: Velocity is the rate at which data is received and used to make decisions. The majority of the time, data is streamed into memory rather than written to the disc. Real-time analysis and responsiveness are required by some internet-connected smart devices because they operate in real-time or almost real-time.
- Veracity: Given the volume, diversity, and speed that Big Data offers, the models built on the data won't be of real value without this attribute. Veracity is the quality of the data produced after processing as well as the credibility of the original data. The system should reduce the effects of data biases, anomalies or inconsistencies, and duplication, among other concerns.
- Value: In the realm of business, value is the most crucial V. Daily information production is vast, but gathering data is not the only way for businesses to make sense of it. Organizations engage in a variety of big data tools because they aid with data aggregation, storage, and insights from raw data that could provide businesses a competitive edge.
What are big data tools?
Despite the fact that big data has apparent advantages for many businesses, 63% of employees, according to Sigma Computing, claim that their solutions don't provide insights quickly enough. The ability to get data insights before they become obsolete may be the biggest issue for many businesses.
Big Data analysis and processing are difficult tasks. These tasks require a comprehensive collection of tools that can not only help you resolve them but also enable you to deliver meaningful outcomes. To handle and extract information from a large number of data sets, Big Data tools are used. When you combine Big Data with powerful analytics, you can effortlessly fulfil business-related tasks. As a result, managing your data using Big Data Tools becomes easy.
Key factors to evaluate big data tools
A diverse range of Big Data tools and technologies are currently on the market. They enhance time management and cost-effectiveness for tasks involving data analysis. However, it can be challenging while choosing the right Big Data tool for your business use case. To help you, we have curated some critical factors that will make your decision to choose a Big Data Tool easier.
- Organization use case & objectives: Your Big Data tool should be able to satisfy both the present and long-term company objectives, just like any other IT asset. Create a list of the primary objectives for your firm and your targeted business outcomes. Then, divide your business goals into quantitative analytics targets. Last but not least, select Big Data tools that give you access to data and reporting features that will aid in the accomplishment of your business goals.
- Pricing: You must be fully informed of all costs involved with the Big Data tool you're considering before finalizing it. These costs may include membership fees, growth costs, the cost of training your employees on the tool, and other expenses. You should be aware of the pricing details before committing to a purchase as different Big Data Tools and technologies have multiple pricing models.
- Easy-to-use interface: Your data teams can spend more time enhancing, implementing, & operating analytics models if they spend less time configuring connectors that link analytics systems to data sources and business software. Big Data tools need to be adaptive to a variety of users. It should be easy to integrate the connectors and simple to interpret the issues and reports for even non-technical staff members.
- Integration support: In order to choose the ideal Big Data Tool for your company, you must determine if a standalone or integrated solution is best. While standalone solutions give you a wide range of choices, integrated solutions let you access analytics using applications that your staff is already accustomed to using. Moreover, prefer tools that offer a wide range of connectors to connect to data sources and destinations.
- Scalability: Machine learning and predictive models frequently need to produce results promptly and cost-effectively. Big Data tools must therefore provide high degrees of scalability for ingesting data and working with massive data sets in production without incurring excessive hardware or cloud service expenses. Big Data tools hosted in the cloud such as Fivetran are quite scalable. These tools aid startups in gaining a competitive edge and surviving periods of rapid expansion. Hence, you can access data more quickly and use analytics for faster decisions.
- Data governance & security: The big data you're working with may contain sensitive data, like personally identifiable information and protected health information that must adhere to privacy regulations. Hence, Big Data tools must include data governance features to assist businesses in implementing internal data standards and adhering to regulatory obligations for data security and privacy. For instance, certain tools now offer the option to anonymize data, enabling data teams to create models based on personal information in accordance with regulations like the GDPR and CCPA.
Top 15 best big data tools
Now that you have understood Big Data and why you need Big Data tools, let’s explore the best Big Data Tools available in the market.
With a zero-maintenance pipeline that guarantees quick data delivery from the source to the data warehouse, Fivetran is a cloud-native Big Data ETL tool that streamlines and simplifies the data analysis process. It enables users to accelerate analytics and shorten the time to insights without the use of complicated engineering, encouraging more effective data-driven decision-making.
Pricing: Offers a 14-day free trial. Supports a consumption-based pricing model. Fivetran now offers a free plan that includes standard plan features and allows up to 500,000 monthly active rows.
Key Features of Fivetran
- Supports more than 300 pre-built connectors for well-known data sources like Facebook, SalesForce, and Microsoft Azure.
- Data exporting and transformation are made simple by pre-built data models.
- To improve the performance of your Fivetran pipeline, you can make use of development platforms, API clients, code sample libraries, and other resources.
- 1M+ daily syncs with 99.9% uptime.
- Offers a completely controlled and automated data migration experience. Provides automated updates, normalization, and control of schema drift.
- Today's most important security and privacy requirements, such as SOC 2 audits, HIPAA, ISO 27001, GDPR, and CCPA, are all met by Fivetran.
2) Apache Hadoop
Apache Hadoop is used to handle big data and clustered file systems. Large datasets are processed using the MapReduce program. LinkedIn, Twitter, Intel, Microsoft, Facebook, and other well-known companies leverage Apache Hadoop. It includes 4 main components: Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce & Hadoop Common.
Key Features of Apache Hadoop
- It is based on a cluster system, allowing for efficient and parallel data processing.
- From one server to numerous computers, it can process both structured and unstructured data.
- Additionally, Hadoop provides its users with cross-platform support.
- Provides rapid access through HDFS (Hadoop Distributed File System).
- Highly flexible and simple to implement with JSON and MySQL.
- High scalability because it can disperse a big volume of data into manageable components.
3) Apache Spark
Another framework used to handle data and carry out multiple activities on a large scale is Apache Spark. It is also used to process data across several machines. It is commonly used by data analysts because it has simple APIs that make it easy to pull petabytes of data. Spark has been leveraged by businesses like Netflix, Yahoo, eBay, and many others.
Key Features of Apache Spark
- Over the Hadoop platform, you can implement batch, & real-time tasks.
- Running an application in a Hadoop cluster is valuable since it can run up to 100 times quicker in memory and 10 times faster on disc.
- Can integrate with Hadoop and use existing Hadoop Data.
- It has built-in Java, Scala, and Python APIs.
- It can operate individually or in a cluster on Hadoop YARN, Apache Mesos, Kubernetes, and the cloud.
- In-memory data processing is offered by Spark, which is far faster than MapReduce's reliance on disc processing.
- Additionally, Spark integrates with HDFS, OpenStack, and Apache Cassandra in both the cloud and on-premises environments, giving your company's big data operations even more flexibility.
4) Apache Kafka
Apache Kafka is a framework for storing, reading, and analyzing streaming data. By separating data streams from systems, Kafka holds the data streams until they are needed elsewhere. Numerous businesses, including more than 80% of the Fortune 100, use Kafka. They consist of Uber, Box, Goldman Sachs, Airbnb, Cloudflare, Intuit, and other companies.
Key Features of Apache Kafka
- It operates in a distributed environment and communicates with other machines and applications using the reliable TCP network protocol.
- Data is distributed over several servers thanks to Kafka's partitioned log model, which enables it to scale beyond the capacity of a single server.
- Failures of the master and the database can be handled by the Kafka cluster. It has the capacity to independently restart the server.
- Kafka can handle any size of real-time data processing to dataflow programming.
- It is an effective method for monitoring operational data. It enables you to gather information in real-time from numerous platforms, organize it into consolidated feeds, and monitor it using metrics.
5) Apache Storm
Apache Storm is an open-source big data software that supports JSON-based protocols. Its fault-tolerant, real-time processing system works with almost all programming languages. Some well-known clients of Apache Storm include Yahoo, Groupon, Alibaba, and The Weather Channel among others.
Key Features of Apache Storm
- In a matter of seconds, it can handle more than a million jobs on the node.
- It has big data tools and technologies that use concurrent calculations across a group of computers.
- Apache Storm topology continues to function until the user turns it off or an unexpected technical issue arises.
- It can operate on JVM (Java Virtual Machine).
- It can be utilized by medium-sized and large-scale enterprises since it is open-source, versatile, and reliable.
- It has low latency. Apache Storm ensures data processing even though messages are missed or cluster nodes fail.
- Supports a variety of use cases, including machine learning, distributed RPC, real-time analytics, log processing, and ETL.
6) Apache Cassandra
Today, a significant number of companies leverage the Apache Cassandra database to manage massive amounts of data effectively. A distributed database management system is used to manage enormous data across multiple servers. It is used by businesses like Netflix, Twitter, Apple, Cisco and many others.
Key Features of Apache Cassandra
- There is no single point of failure because data is duplicated across numerous nodes. Data saved on other nodes will still be usable even if a node malfunctions.
- Additionally, data can be replicated throughout different data centres. Data can therefore be recovered from other data centres if it is lost or damaged in others.
- It offers a simple query language, thus switching from a relational database to Cassandra won't cause any problems.
- It contains built-in security features including data backup and recovery capabilities.
- Nowadays, Cassandra is frequently utilized in IoT real-world applications where massive data streams from gadgets and sensors are generated.
7) Apache Hive
Apache Hive is a data warehouse infrastructure based on SQL and enables users to read, write, and manage petabytes of data. Facebook originally developed it, but Apache took it under its wing and has been advancing and maintaining it ever since.
Key Features of Apache Hive
- Runs on top of Hadoop and processes structured data and is used for data summarization and analysis.
- Apache Hive processes enormous volumes of data using HQL (Hive Query Language), a language that is comparable to SQL and is converted into MapReduce jobs.
- Allows client applications created in any language - Python, Java, PHP, Ruby, and C++.
- Hive Metastore(HMS) is typically used to store the metadata, which considerably cuts down on the time needed for the semantic check.
- The performance of queries is improved by Hive Partitioning and Bucketing.
- It is a powerful ETL tool that supports online analytical processing.
- It offers support for User Defined Functions to accommodate use cases that built-in functions do not handle.
8) Zoho Analytics
You can quickly and easily create amazing data visualizations, perform visual data analysis, and find hidden insights with the help of Zoho Analytics. Big businesses like Hyundai, Suzuki, IKEA, HP, and others rely on it. To assist you in getting started quickly, this application includes a variety of pre-built visuals.
Pricing: Offers 15 day free trial and 4 pricing plans.
Key Features of Zoho Analytics
- It provides seamless analysis of the data with exceptional end-to-end insights through simple connectors, pre-built algorithms, and intelligent data blending.
- Assists in monitoring important company metrics, evaluating historical trends, spotting anomalies, and uncovering hidden insights.
- Converts enormous raw data files into useful reports and dashboards.
- Robust data import and integration APIs are available, allowing for the quick development of a custom connector.
- You can give your clients comprehensive reports thanks to an easy drag-and-drop interface.
Cloudera is one of the quickest and safest Big Data technologies available at the moment. It was initially created as an open-source edition of Apache Hadoop geared toward enterprise-class deployments. You can simply extract data from any environment with this flexible platform.
Pricing: Offers various pricing models and charges on Cloudera Compute Unit (CCU) used.
Key Features of Cloudera
- Enables businesses to use self-service analytics to evaluate the data in hybrid and multi-cloud environments.
- You can manage and deploy Cloudera Enterprise on AWS, Azure, and Google Cloud Platform.
- Easily transferable between different clouds, including private options like OpenShift.
- Enables the self-service provision of integrated, multifunctional solutions for data centralization and analysis.
- To boost efficiency and cut expenses, it can automatically scale workloads and resources up or down.
- Without altering underlying data structures or tables, Cloudera Data Visualization allows its customers to model data in the virtual data warehouse.
RapidMiner intends to provide data specialists of all skill levels with the tools to quickly prototype data models and carry out machine learning algorithms without having any coding knowledge. Through a process-focused visual design, it combines everything from data access and mining through preparation and predictive modelling.
Pricing: Free version includes 1 logical processor & 10,000 data rows. A free educational license is also provided. For other pricing options, you need to request them.
Key Features ofRapidMiner
- Users can access more than 40 different file types through URLs.
- Access to cloud storage resources like AWS and Dropbox is available to users.
- For easier analysis, Rapid Miner offers a visual display of numerous findings across time.
- RapidMiner was created using Java and can be readily connected with other Java-based applications.
- It also has Python and Java modules that can be modified with code.
- Offers the comfort of cutting-edge data science tools and algorithms.
OpenRefine, formerly known as Google Refine, is a well-known open-source data tool. It is one of the robust Big Data tools that is used for data cleansing and transformation. Large datasets can be handled without any issues. It also enables the addition of other data and web services.
Key Features of OpenRefine
- Complex data computations are possible with Refine Expression Language.
- Users can quickly explore big datasets.
- It executes cell transformations and manages table cells with different data values.
- It works with external data and expanded web services.
- Data is always kept private on your machine by OpenRefine, and you can also share it with other team members.
Apache Kylin is a large data analytics and distributed data warehouse platform. It offers a processing engine for web analytics that can handle very huge data volumes. It can scale effortlessly to manage enormous data loads as it is built on top of other Apache technologies like Hadoop, Hive, Parquet, and Spark.
Key Features of Kylin
- For the multidimensional analysis of massive amounts of data, Kylin offers an ANSI SQL interface.
- It integrates with BI solutions such as Tableau, Microsoft Power BI, and others.
- Kylin supports JDBC, ODBC, & RestAPI interfaces that make it possible to connect to any SQL application.
- Capability for developing user interfaces on top of the Kylin core.
- It leverages pre-calculation to obtain a head start on SQL execution, making it faster than traditional SQL on Hadoop.
A distributed stream processing system called Apache Samza was created by LinkedIn and is currently an Apache-led open-source project. Samza enables users to develop applications that can process data from sources like HDFS and Kafka in real-time. It is used by numerous businesses, including Redfin, Slack, and LinkedIn, to name a few.
Key Features of Samza
- Additionally to supporting a standalone deployment, the system can also be used to run on top of Hadoop YARN or Kubernetes.
- Handles terabytes of data for quick data processing with minimal latency and great throughput.
- You can transfer or restart one or more containers of your cluster-based applications from one host to another using the Container Placements API without having to restart your application.
- Built-in Hadoop, Kafka, and other data platform integration.
- Fault-tolerant features intended to facilitate quick system failure recovery
- Supports the capability to drain pipelines to enable incompatible intermediate schema updates
Lumify is an open-source platform for big data fusion, analysis, and visualization that facilitates the generation of valuable insights. Through a variety of analytical capabilities, it helps users in finding connections and explores relationships in their data.
Key Features of Lumify
- It enables automatic layouts for both 2D and 3D graph representations.
- Link analysis between graph objects, multimedia analysis, and real-time collaboration via a number of projects or workspaces are some of the other features.
- For textual content, photos, and videos, it includes particular ingest processing and interface capabilities.
- You can arrange work into a number of projects or workspaces using its spaces function.
- The infrastructure of Lumify enables the integration of new analytical tools that will monitor modifications and support analysts
- For geospatial analysis, Lumify enables you to incorporate any open layers-compatible mapping systems, such as Google Maps or ESRI(Environmental Systems Research Institute, Inc.).
With Trino (formerly PrestoSQL), businesses of all sizes and levels of cloud adoption can benefit from quicker access to all of their data. Trino is currently used by tens of thousands of businesses, including LinkedIn, Netflix, Slack, Comcast, AWS, Myntra, Razorpay and many others. Trino initially intended to query data from HDFS. It natively executes queries in Hadoop and other data repositories, enabling users to query data regardless of where it is stored.
Key Features of Trino
- Supports SQL, used in data warehousing and analytics for data analysis, data aggregation, and report generation.
- Designed for both extensive batch queries and ad-hoc analytics.
- Simple to interface with BI systems like Tableau, Power BI, etc.
- The flexible design of Trino enables simultaneous analysis across many data sources.
- Storage and computing are independent of one another and can scale separately.
- Trino uses both established and cutting-edge methods for distributed query processing such as Java bytecode generation, in-memory parallel processing, pipelined execution across cluster nodes, and many more.
For the years to come, Big Data tools will maintain their market dominance across almost all industries and market sizes. There are plenty of solutions available on the market right now; all you need is the proper strategy and the right tool for the job.
This in-depth article covered nearly all aspects of Big Data, including its use, well-known Big Data tools including free source and paid tools, and important factors on how to pick the right Big Data tool. Before purchasing a commercial edition of any large data processing tool, it is usually good to first try out the trial version and, if at all feasible, speak with current users to get their reviews.
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