Apache Tajo

Apache Ambari is an all-in-one software project that permits administrators to provision, manage and monitor a Hadoop cluster and also providing the possibility to integrate Hadoop with the existing enterprise infrastructure. Apache Ambari is making the Hadoop management simpler courtesy of having easy-to-use web UI, and restful APIs adds more support.

Apache Ambari is facilitating application developers and system integrators to have ease of integration with Hadoop management, provisioning, ad monitoring capabilities, and with the extensive dashboard to track the health and status of the Hadoop clusters. Other specs include step-by-step wizard, configuration handling, system alerting, metrics collection, multiple operating system support, and more to add.

Cloudera Distribution for Hadoop offers the comprehensive solution needed to handle big data workloads across your business, whether on-premises or in the cloud. With this solution, you get an integrated platform that scales up and down with ease and performance that runs everything from petabytes to billions of records in real-time.

The same integrated experience can also be applied to large data warehousing in your infrastructure that runs workloads like Presto, Impala, Spark SQL, MapReduce, Kafka, and more. With enterprise-grade continuous availability, advanced security, management, and backup features, Cloudera Enterprise delivers proven performance for every mission-critical project. The goal is to create a distributed computing platform based on industry standards that supports data management, sharing, and security.

This framework allows you to develop applications that store and analyze large amounts of data across many computers in clusters. It includes several sub-projects, including Hadoop Distributed File System, Hadoop MapReduce, and Apache Hive. All in all, Cloudera Distribution for Hadoop is a great solution that you can consider among its alternatives.

Apache Kudu is a distributed data storage and fast analytics engine that you can consider as a new system for managing unstructured data. It’s designed to solve the challenges of scaling out storage, optimizing queries, and much more. The software manages data in column-oriented formats, similar to Vertica or a distributed version of the proprietary BigTable database. The key innovation is its division of data into “partitions,” which are spread across an arbitrary number of servers.

This storage format is designed for efficient and fast processing of large volumes of data. Kudu is a distributed deep-compression structure for storing data, designed for low latency read/write access patterns. Apache Kudu also provides a high-performance, distributed analytics processing framework. The Analytics Engine Enables fast, interactive, multi-dimensional analysis on top of Apache Hadoop. It is created with modern architecture, making it easy to build and operate in the cloud while also supporting existing relational model workloads.

Moreover, it uses a set of simple, clean APIs that allow you to access and manage data stored in files and tables. Last but not least, the solution also supports a variety of advanced analytic use cases and machine learning algorithms, allowing you to create rich end-user experiences over the same datasets that power your advanced analytics.

Apache Avro is a comprehensive data serialization system and acting as a source of data exchanger service for Apache Hadoop. You can either use these services either independently or used together, and it is making things a lot easier when it comes to exchanging big data between programs regardless of the language. Apache Avro is pretty similar to thrift and protocols, but it does not require a code generation program when dealing with schema changes.

Apache Avro is entirely a row-oriented remote procedure and serialization framework and has been using JSON for defining types and protocols, and all the data will be serialized in compact binary format. In the Apache Hadoop, Apache Avro permits support for both serialization format and wire format for persistent data and communication between Hadoop nodes and client program, respectively.

Apache Zeppelin is a web-based notebook for capturing, exploring, and visualizing data. It aims to support structured and semi-structured data, such as text files, tables in spreadsheets, and relational databases, as well as all types of dynamic content like shell scripts or Python code. It enables users to visually explore their data with rich tables, charts, and histogram displays and then automatically generate higher-level abstractions such as summaries of individual observations in simple prose.

It is an open source web-based notebook for interactive data analytics, with support for Apache Spark, Apache Flink, and other distributed data processing systems. It provides a secure environment for interactive data analytics with support for rich data visualization and metadata-driven programming. It aims to provide a Jupyter-like experience for analyzing, processing as well as visualizing large amounts of data in a distributed fashion. It also supports multi-user collaboration and can be configured for read-only access or read/write access for select users or groups. It enables users to execute data queries and connect to various data sources and visualize the analyzed data using its rich user interface.

IBM dashDB, now named Db2 Warehouse, is the next-generation analytics warehouse. The role this warehouse plays in analytics is to ensure that data can be quickly analyzed for intelligent processing. It also provides the ability to analyze historical data, support analytic jobs spanning multiple nodes, and provide high-performance responsiveness when querying your data. The Db2 Warehouse extends the capabilities of existing IBM suites to facilitate much more complex queries against data.

Moreover, it has a great query performance by being able to store, retrieve and analyze millions of records per second, can support analytic queries spanning nodes, and allows for its entire query history to be stored in a single node. The Db2 Warehouse includes all this functionality within a single platform, providing an integrated package with an intuitive graphical interface to provide maximum usability for DBAs and analysts alike. All in all, Db2 Warehouse is a great solution that you can consider among its alternatives.

Apache Oozie is an all-in-one trusted and server-based workflow scheduling system that is aiding you in managing Hadoop jobs more conveniently. The platform provides workflows which are actually a collection of control flow and action nodes with a directed acyclic graph. The primary function of this utility is to manage different types of jobs, and all the dependencies between jobs are specified.

Apache Oozie is currently supporting a different type of out-of-the-box Hadoop box because of the integration with the rest of the Hadoop stack. Apache Oozie seems to be a more extensible and scalable system that makes sure that Oozie workflow jobs are adequately triggered with the help of the availability of time and data. Moreover, Apache Oozie is a reliable option to have in starting, stop, and re-run jobs, and even you run failed workflows courtesy of having the support of action nodes and control flow nodes.

Apache HBase is an open-source platform that is based on non-relational databases modeled written in Java. The platform provides extensive support with easy access to real-time and big random data whenever you need them. This project is hosting a large number of tables, rows, and columns and is just like Bigtable, surfacing the significant amount of distributed data storage, so you will on top of Hadoop and HDFS. Now backing protobuf, binary data encoding options, and XML is easy because of the thrift gateway and restful web service provided by Apache HBase.

Apache HBase is supporting to perform the task related to exporting metrics with the help of Hadoop metrics subsystem to files, or Ganglia, or use JMX. The multiple features include linear and modular scalability, strictly consistent reads and write, automatic failover support, block cache, bloom filters, real-time queries, convenient base classes, automated & configurable sharing of tables, and more to add.

Cloudera Enterprise 6 is an enterprise-grade distribution of the world’s leading big data platform that accelerates modern data management, machine learning, and AI, IoT, and business intelligence. The platform is leveraging businesses by integrating the world’s leading technologies – Apache Hadoop, Apache Spark, Solr, Impala, and HDFS.

Cloudera Enterprise 6 is the only platform that enables you to scale big data and machine learning at all levels of the stack, from IoT sensors to real-time stream, business intelligence, and advanced analytics. With Cloudera Navigator, it has been a lot convenient for deploying, managing, and monitoring Apache Hadoop and Apache Spark clusters. Moreover, you have a comprehensive, unified user interface that simplifies all aspects of managing clusters and gets a real-time and historical operational and analytical view of your Hadoop and Sparks clusters.

Apache Mahout is a distributed linear algebra framework that is under the supervision of Apache software that paves the way to have free implementations. The platform provides Scala DSL designed to let mathematicians, data scientists, and statisticians get done with their own implementation of algorithms. Apache Mahout is extensible to extend to various distributed backbends and is providing expediency for modular native solvers for CPU, GPU, or CUDA acceleration.

Apache Mahout comes with Java or Scala libraries for common maths operations and primitive Java collections. There is Apache Mahout-samsara acting as DSL that allows users to use R-like syntax, so concise and clear you are as far as expressing algorithms are concerned. Moreover, you can do active development with the Apache Spark engine, and you are free to implement any engine required. Adding more, Apache Mahout is adequate enough for web techs, data stores, searching, machine learning, and data stores.

Hadoop HDFS is distributed file system that allows you to run commodity hardware. The software delivers ease of access to the application data and is suitable for applications that require large data sets. It facilitates you with high throughput in order to access the application data, and more likely, it also provides built-in support for applications comprising of large data sets. Because of its leniency in a few POSIX requirements, it enables streamlining your access to file system data.

If you look into the original infrastructure, Hadoop HDFS is built as infrastructure for the Apache Nutch web search engine project. Since it is acting as an open-source distributing processing framework, so it is a much convenient option in managing data processing and storage for big applications. Moreover, it is a valuable option in managing a comprehensive amount of data and aids in big data analytics applications.

SAP Business Warehouse (SAP BW) is a model-driven data warehousing product based on the SAP NetWeaver ABAP platform that provides a single, unified interface to consume and build data from multiple sources. It also includes integrated self-service capabilities that allow users to start using the system without assistance from IT. The solution has features for building a better business in all areas of today’s organizations, including ERP packages, transactional systems, customer relationship management systems, and other business intelligence tools.

SAP Business Warehouse can design reporting procedures in an intelligent way with easy modeling techniques and without lengthy development time. This solution is a component of SAP NetWeaver that gives you the ability to build, manage and consume data warehouse models that are based on SQL concepts. This means that SAP Business Warehouse is designed to make it easy to define database models, define queries, generate query code, and load data into data marts or data warehouses.

Moreover, you can perform all these tasks without any additional development time or cost because they are based on widely used SQL concepts. Using SQL also allows you to access Business Intelligence tools that are normally designed using SQL, such as Data Mining. All in all, SAP Business Warehouse is a great solution that you can consider among its alternatives.

Analytic Solver is a simple yet powerful point and clicks simulation software that offers risk analysis, prescriptive analytics, data mining, text mining, forecasting, and predictive analytics. It is an advanced level solution that solves every type and size of optimization issues to better allocate rare resources. Unlike all the other similar solutions, it algebraically analyzes your model structure and maximally exploit several cores in your PC.

With the help of this solution, you can easily analyze and control risk, create optimal plans as well as resource allocation decisions. The data mining feature of the solution is quite impressive that give you easy to use text mining and predictive analytics on Excel. You can sample data from SQL databases, Power Pivot, and Apache Spark, explore your data visually, transform data, and apply the full range of time services.

Analytic Solver comes with a simple and easy to understand dashboard where you can access all tools and features. Analytic Solver’s prominent features conventional optimization, simulation, data mining, and much more.

3PL Warehouse Manager is a cloud-based Warehouse Management Service solution that transforms your paper-based business into 3PL service leaders. It helps you shorten your manual errors and lost inventory resulting in improved warehouse efficiency. Cloud-based API integration allows order automation with shopping carts. You can track your items, direct put a way to store inbound items, create repeatable and scaleable customer workflows with the 3PL Warehouse Manager.

Its proprietary 3PL-centric billing system saves you from manual billing, lost revenue, and long revenue cycles by segmenting inventory, billing, and reporting. The dashboard keeps you updated with accurate real-time information. It features on-demand reporting, automated customer notification about inventory, API/EDI integrations for eliminating paper processes, and custom integration of your proprietary programs. All in all, the 3PL Warehouse Manager adds more value to your WMS and grows business by a huge margin.

Apache Pig is a dynamic and resounding platform that allows high-level program creation that is run on Apache Hadoop. This extensive platform is suitable for analyzing large data sets comprised of high-level language in order to express data analysis platform. More likely, you have infrastructure that is designed to evaluate these programs. Apache Pig is processing the emendable structure that will do substantial parallelization that paves the way to handle large data sets with ease.

Apache Pig infrastructure comes with the compiler, which then is crucial in producing sequences of the map-reduce program, but this thing required a large-scale parallel implementation that already present. The contextual language of Apache Pig is valuable in providing the ease of programming, optimization possibilities to encode tasks, and lastly, extensibility to create own function in order to have special-purpose programming.

HortonWorks Data Platform enables developers to develop scalable and secure enterprise applications using open source technologies. You can use this solution to a wide range of industry problems, including enterprise reporting, business intelligence, transaction processing, high-performance computing, online analytic processing, search, visualization, social networking, web indexing, multimedia indexing, document analysis, content analytics, medical imaging, telemedicine, remote sensing, big data mining, social network analysis, mobile devices, gaming, social networking, machine learning, etc.

With this service, you get the proven scalability and reliability of Apache Hadoop. The latest release of HDP includes capabilities for both long-term storage and fast retrieval of structured and unstructured data. In addition, it provides advanced SQL support for querying large datasets. You also get security for users’ sensitive data by implementing role-based access control and LDAP integration.

Its core algorithm offers pre-integrated security, integrated distributed cache, full SQL query support, easy deployment of key-value stores, ability to execute batch processes in a parallel mode, advanced application management capabilities, rich text editing, and improved graphical interface for query builders. All in all, HortonWorks Data Platform is a great solution that you can consider among its alternatives.