HortonWorks Data Platform Alternatives

Google Cloud Dataproc is one of the best scalable and managed services that you can use to run Apache Spark, Presto, and more than 30 other tools and frameworks. It handles data exceptions while ensuring that no part of the processing fails. The resulting end-to-end workflow guarantees a fast response time while allowing flexible combinations of streaming and batch jobs.

This modernizes your data processing by accelerating analytics processing in serve less environment. A great advantage is that the data scientists and data analysts can easily perform data science jobs via native integration. While creating a data cluster, you can enable Hadoop Secure Mode via Kerberos by adding a Security Configuration.

Google Cloud Dataproc service allows you to create managed clusters that can scale from three to hundreds of nodes. Moreover, it lets you take the open-source tools, algorithms, and programming languages that you use today but makes it easy to apply them on cloud-scale datasets. All in all, Google Cloud Dataproc is a great solution that you can consider among its alternatives.

HPE Ezmeral Data Fabric software provides enterprise-class storage and processing for both structured and unstructured data. Its architecture is able to address three primary concerns with current enterprise data fabric solutions; scaling out and horizontal scalability, ability to retain high availability in case of failures, and supporting petabyte-scale deployments.

The architectural patterns used by HPE Ezmeral Data Fabric have also been deployed at the Massively Parallel Supercomputing Centers across the globe. These large-scale clusters consist of over one hundred compute nodes interconnected with Ethernet networks and connected to storage at close to 10 petabytes per day.

Its network hardware abstracts away all complexities from users so that they can focus on applications instead of hardware implementation details. Moreover, you can also accelerate time to insights with a single trusted data source and secure data sharing across traditional and modern data analytics apps and tools with this service. All in all, HPE Ezmeral Data Fabric is a great solution that you can consider among its alternatives.

Amazon EMR is a service that makes it easy to provision, operate, and scale Apache Hadoop clusters. It allows you to build clusters of machines with no prior cluster management experience. Using Amazon EMR, you can easily scale your cluster by adding or resizing nodes or specifying different cluster configurations for different workloads.

The solution provides high availability for Amazon AWS clusters by providing point-in-time recovery of the state of your cluster at any point in time. With the elasticity of EC2 instances, you can rapidly add or remove nodes to or from your cluster to handle workloads. Amazon EMR also provides secure access to Hadoop clusters through encryption and tokenization of security credentials.

It automatically resizes your cluster for the best performance at the lowest possible cost. With its Managed Scaling, you can specify the minimum, and maximum compute limits for your clusters, and the EMR automatically resizes them for best performance and resource utilization. All in all, Amazon EMR is a great solution that you can consider among its alternatives.

SingleStore DB is a high-performance SQL compliant relational database management tool that offers data processing, ingesting, and transaction processing. The new development paradigm based on multiple streams of transactions simplifies both the server implementation and client access to the database. You can extend your database to interact with applications as well as process it as a collection of stored procedures. These innovations enable you to rapidly deploy complex business applications while preserving existing IT investments.

SingleStore DB integrates tightly with Java/J2EE technologies and other languages such as C++, Python, Ruby, PHP, etc. You can run it on any hardware platform, including small single-board computers. This is the ideal all-in-one database for operational analytics and AI-powered applications that requires fast data ingest, high-performance queries, and elastic scaling with familiar relational SQL. All in all, SingleStore DB is a great solution that you can consider among its alternatives.

Mode Analytics is a collaborative platform for data analytics that utilizes real-time adaptive analytic models. The platform provides zero-latency processing to synthesize unstructured and diverse data into understandable information for rapid decision-making. Mode Analytics can be deployed across diverse industry segments, including aerospace, health care, manufacturing, financial services, defense, retail, transportation, telecommunications, education, government, and life sciences.

This is the perfect solution for teams as they can build dashboards for annual revenue, then use chart visualizations to identify anomalies quickly. You can create polished reports and share analyses with teams for collaboration. All in all, Mode Analytics is a great solution that you can consider among its alternatives.

Microsoft HDInsight is an analytics solution that provides you with easy access to Hadoop cluster provisioning and seamless connection to Big Data and machine learning workloads in the cloud. You can consider this as an evergreen solution for large-scale architecture and having the flexibility to adjust resources when your needs change. This data distribution solution offers numerous benefits, including improved security, stability, reliability, scalability, manageability, performance, availability, etc.

With this platform, security can be improved through proper management of shared files and ensuring secure access between applications running on the same nodes. Secure your cluster with virtual network isolation and control outbound traffic using Firewall and VNet. Moreover, you can also use your own encryption keys to protect end-to-end data with encryption in transit. All in all, Microsoft HDInsight is a great solution that you can consider among its alternatives.

Platfora, now acquired by Workday, is a data analytics platform that provides analytical tools for efficiently processing information through AI and ML. It can process and visually interact with the petabyte-scale data. These capabilities can be applied to structured data as well, allowing you to visualize information by selecting entities of interest. Platfora’s approach is more general than just being applicable to a particular computing environment.

Moreover, when combined with business analytics, it enables self-service, interactive, native access to raw data at scale so you can see correlations between behaviors, actions, and results across every touchpoint in the business. All in all, Platfora is a great solution that you can consider among its alternatives.

Domino Data Lab provides a data analytics platform that helps in research, model deployment, and collaboration for teams. It allows you to create models using tools like Scikit-Learn, Apache Spark, or other machine learning libraries. The models can then be used to solve various business problems through visual exploration and predictive modeling. Highlighting features include interactive exploratory notebooks for working with large datasets, storage of large datasets, ML analysis via a browser-based user interface, integration with tools like H2O, TensorFlow, etc.

Elastically scale compute resources, including CPU, GPU, and Spark clusters, on-demand. Moreover, you can automatically distribute all required packages and dependencies without any DevOps headaches and wasted time. Gain visibility into all data science work being conducted across the organization and secure and auditable data science operations with permission, SSO, credential propagation, and more. All in all, Domino Data Lab is a great solution that you can consider among its alternatives.

Alpine Chorus provides analytics by hosting SQL queries in Apache Spark and returning analytic results via Kafka Streams. It includes support for ingesting large volumes of raw data via bulk ETL and scalable analytical frameworks, such as MLlib, PySpark, TensorFlow, or Google’s BigQuery.

Alpine Chorus integrates seamlessly with Spark applications through Jupyter notebooks and visualizations powered by Dash. With its collaboration features, you can harness the creativity of the entire team and maintain transparency, security, version control, and audibility. Combine AutoML, intuitive drag-and-drop workflows, and embedded Jupyter Notebooks that make creating and sharing reusable modules easy.

Highlighting features include full support for Python Spark streaming, data frame transformations, dimensional reduction, projection, joining, windowing data warehouse compatibility, prebuilt UDFs for data warehouse projects stored procedures, and provisioned functions to add tailored computation to existing pipelines. Moreover, built-in AutoML support is also there with the easy creation of models. All in all, Alpine Chorus is a great solution that you can consider among its alternatives.

Amazon Elastic MapReduce (EMR) is a large-scale data cluster processing platform that simplifies complicated data into information. EMR executes map-reduce jobs in the Amazon EC2 environment. Specifically, it presents an end-to-end design for implementing a fully operational big data pipeline that can ingest large datasets into EMR and perform iterative machine learning computations using this solution.

The architecture leverages two compute components to solve this problem: a low-latency database with local persistent storage for short-lived queries and a long-lived cluster manager that manages compute resources. The solution is built with scalability and durability in mind, using concepts from NoSQL databases and highly available services in the cloud.

To reduce cost and improve reliability, it also introduces failover strategies to prevent outages of individual components. The solution is implemented on top of Hadoop’s core component Apache HBase, but it can be easily extended to any language or runtime environment. All in all, Amazon Elastic MapReduce is a great solution that you can consider among its alternatives.

Sybase IQ is a relational database NoSQL solution that you can use for unstructured data management. A primary focus of IQ is its full-text indexing capabilities, but its fundamental principles are equally applicable to other applications such as full-text search. Sybase IQ provides a way to take advantage of this innovation without compromising on flexibility or other desirable features. There is also the option of integrating this software with other components, such as text analysis engines, textual clustering tools, or report generators.

With this tool, you easily gain more speed, security, and power, all while having data warehousing and big data analytics. Talking about security, you get the transport layer, database, and column encryption, as well as LDAP and Kerberos authentication level of security for data. All in all, Sybase IQ is a great solution that you can consider among its alternatives.

InfluxDB is a time-series database that aims to make data analytics simpler by focusing on simplicity itself. By decoupling storage and query execution, it delivers high availability, linear scalability, and rich queries at very low latency. The highlights of this platform are a powerful API and toolset, a high-performance time-series engine, and a huge library of developers and community. It uses Kubernetes as the deployment engine and helps to get a distributed file system-based key-value store called MPFS to implement the consistency and write throughput guarantees of MPFS in Kubernetes.

This work aims to establish a general model for building such systems, including components like these file systems and how they can be deployed using orchestration tools. With this tool, you can easily build real-time IoT applications and cloud services with less coding hassle. All in all, InfluxDB is a great tool that you can consider among its alternatives.

Greenplum HD allows you to build a scalable data warehouse system that can hold very large amounts of data, query it at high speed, and run interactive reports on it. You can consider this as a hybrid database solution, and this approach provides a unified view of the data that simplifies analysis and aggregation. It makes use of shared disk and caching technology to improve performance. The solution can handle petabyte-scale levels of data workloads and scales interactive analytics to large datasets without slowing sown performance output.

It also has sophisticated multi-tier security mechanisms to protect data from accidental or malicious loss. The storage subsystem uses array-based RAID levels for efficient access to disks. Moreover, the indexing is accomplished by extending standard PostgreSQL arrays with custom logic to support large databases. The metadata is stored in tables, and each row stores both the raw data and its semantic meaning. All in all, Greenplum HD is a great tool that you can consider among its alternatives.

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.

Sense is a large-scale data analytics and collaboration platform that enables advanced AI by delivering customizable machine learning algorithms and powerful visualization tools. The platform offers state-of-the-art features to power high-performance and scalable AI systems that enable your clients to move data to the next level of understanding.

The platform allows you to make advanced and complex models quickly available for different applications in real-time. In addition, it offers pre-trained models to enable fast prototyping of custom models and deliver ML predictions at low latency. You also get to collaborate with experts by connecting their model libraries with Sense’s ML ecosystem. All in all, Sense Platform is a great solution that you can consider among its alternatives.