IRI Data Manager vs. SQLWays Comparison Chart

One Solution to Simplify File Management and Orchestration Across Edge, Data Center, and Cloud Storage PeerGFS is a software-only solution developed to solve file management/file replication challenges in multi-site, multi-platform, and hybrid multi-cloud environments. With over 25 years of experience in geographically dispersed file replication, we help organizations: - Improve availability through Active-Active data centers (on-premises and/or in the cloud) - Protect data at the Edge with Continuous Data Protection to the data center - Increase productivity for distributed project teams with fast, local access to file data Today’s always-on world requires real-time data infrastructure with 24x7x365 availability. PeerGFS works with the storage systems you already have deployed and support: - High volume data replication between well-connected data centers - Wide area networks with limited bandwidth and higher latency PeerGFS is easy to install and manage.

RaimaDB is an embedded time series database for IoT and Edge devices that can run in-memory. It is an extremely powerful, lightweight and secure RDBMS. Field tested by over 20 000 developers worldwide and has more than 25 000 000 deployments. RaimaDB is a high-performance, cross-platform embedded database designed for mission-critical applications, particularly in the Internet of Things (IoT) and edge computing markets. It offers a small footprint, making it suitable for resource-constrained environments, and supports both in-memory and persistent storage configurations. RaimaDB provides developers with multiple data modeling options, including traditional relational models and direct relationships through network model sets. It ensures data integrity with ACID-compliant transactions and supports various indexing methods such as B+Tree, Hash Table, R-Tree, and AVL-Tree.

Bacula Enterprise delivers backup and recovery for demanding enterprise, high-performance computing (HPC), defense, government and research environments. Developed by Switzerland-based Bacula Systems, it protects physical, virtual, container, database, SaaS and cloud workloads through one centrally managed platform. Keep backup data and recovery operations under your control. Bacula supports fully on-premises deployment without mandatory cloud control planes, call-home licensing or continuous internet connectivity. Organizations can retain control over storage location, infrastructure, encryption keys and recovery procedures to support data sovereignty and operational independence. Build layered ransomware resilience with physically air-gapped tape and offline copies, immutable storage options, encryption, multi-factor authentication, granular access controls and file integrity verification. Security capabilities can be configured around defense, critical infrastructure and other sensitive environments. For petabyte-scale HPC and research datasets, Bacula offers HPC Accelerator, parallel backup processing and integrations with Lustre, GPFS and ZFS. Protect large file populations while combining disk, tape libraries, object storage and cloud targets. Licensing is not based on protected data volume. Virtualization and cloud VM coverage includes VMware vSphere/ESXi, Hyper-V, Proxmox, Nutanix AHV, XenServer, XCP-ng, KVM, QEMU, Libvirt, Red Hat Virtualization, OpenStack, Amazon EC2 and Azure VMs. Container protection covers Kubernetes, Red Hat OpenShift and Docker. Database protection includes Oracle, SQL Server, PostgreSQL, MySQL, Percona for MySQL, MariaDB, SAP HANA, Sybase ASE, IBM Db2, MongoDB, Hadoop HDFS and InterSystems IRIS, plus Amazon RDS integration. Additional workloads include Microsoft 365 and Google Workspace. Automated scheduling, monitoring, reporting, deduplication, compression and BWeb web-based administration help teams manage distributed infrastructure. Recovery options include files, application data, virtual machines and bare metal recovery for supported systems. Built on an open-source core, Bacula Enterprise combines open source foundation with enterprise-only plugins, security capabilities, management tools and commercial support. This open core approach provides transparency while extending the platform for complex, large-scale and security-sensitive environments.

Semarchy Data Platform (SDP) is a unified data management platform that delivers trusted enterprise data as governed, reusable Data Products. Instead of stitching together separate tools for master data, data quality, integration, and governance, SDP brings these capabilities together in one platform, by design. Every asset (entities, rules, pipelines, APIs, and semantic models) belongs to a Data Product with clear ownership, a versioned lifecycle, and self-describing metadata. Teams build once and reuse across operational systems, analytics, and applications. DataOps is built in: design in VS Code, version in Git, and deploy through CI/CD with testing and rollback. AI-assisted design speeds up modeling and rule configuration, while governance, lineage, and access control stay part of every step. Deploy as SaaS, self-hosted, or natively in Snowflake. The result: less tool sprawl, faster delivery, and data the business can trust.

QuantaStor is a unified Software-Defined Storage platform designed to scale up and out to make storage management easy while reducing overall enterprise storage costs. With support for all major file, block, and object protocols including iSCSI/FC, NFS/SMB, and S3, QuantaStor storage grids may be configured to address the needs of complex workflows which span sites and datacenters. QuantaStor’s storage grid technology is a built-in federated management system which enables QuantaStor servers to be combined together to simplify management and automation via CLI and REST APIs. The layered architecture of QuantaStor provides solution engineers with unprecedented flexibility and application design options that maximizes workload performance and fault-tolerance for a wide range of storage workloads. QuantaStor includes end-to-end security coverage enabling multi-layer data protection “on the wire” and “at rest” for enterprise and cloud storage deployments.

"Parasoft delivers an AI‑powered software testing platform that helps organizations continuously release high‑quality software. Our solutions support embedded and enterprise teams by integrating code analysis, testing, virtualization, and coverage into the delivery pipeline to improve security, reliability, and compliance while reducing cost and effort. Parasoft C/C++test provides static analysis, unit testing, code coverage, and requirements traceability for C and C++ applications. It integrates with Eclipse and Visual Studio, supports CI/CD automation, and is TÜV‑certified for safety‑ and security‑critical systems. Parasoft C/C++test CT is a scalable, compliance‑ready solution for C and C++ teams. It integrates into CI/CD workflows, supports open‑source unit testing frameworks, containers, VS Code, Bazel build systems, eliminates IDE dependencies, and is TÜV‑certified for safety‑ and security‑critical development."

Veeam Data Platform is a unified data resilience solution that helps organizations recover from cyberattacks and outages, and protect workloads across hybrid and multicloud environments – all without vendor lock-in. The platform includes built-in security capabilities designed to mitigate ransomware and other data-loss threats, including AI-backed threat detection across a broad range of workload types. This is intended to help teams identify and respond to threats before they disrupt operations. Veeam Data Platform supports workload portability across hypervisors, cloud providers, and on-premises environments, allowing organizations to move or recover data across infrastructure without being tied to a specific vendor or platform. For organizations looking to simplify deployment, Veeam Software Appliance provides a pre-built, hardened, and highly available option, reducing the manual setup and maintenance work required from IT teams. Veeam Data Platform is used by over 550,000 customers worldwide, including 82% of the Fortune 500.

SCIKIQ is an AI-native Data & Intelligence Platform designed to help enterprises make their data trusted, governed, connected, and ready for AI in weeks rather than years. Recognized by Forrester, NASSCOM League of 10, YourStory Tech30, Inc42, and DataIQ, SCIKIQ supports enterprises across the USA, India, UK, and UAE. SCIKIQ brings together Data Integration, Data Quality, Data Governance, Metadata Management, Data Lineage, Semantic Intelligence, Knowledge Graphs, Conversational Analytics, Generative AI, Data Products, and AI Agents within one unified platform. Unlike traditional data platforms that often require extensive replatforming or migration, SCIKIQ works with an enterprise’s existing technology ecosystem. Organizations can connect SAP, Salesforce, Oracle, Snowflake, Databricks, AWS, Azure, GCP, data lakes, data warehouses, and enterprise applications through 200+ pre-built connectors, without rip-and-replace. Contextual Intelligence at the Core SCIKIQ goes beyond connecting data by helping AI understand the business context behind it. Its semantic intelligence layer brings together business terminology, KPI definitions, metadata, lineage, ownership, business rules, ontologies, and relationships to create a trusted context layer for enterprise analytics and AI. Business users can ask questions in natural language, investigate KPIs, identify root causes, and generate insights without writing SQL. Data teams gain enterprise-grade capabilities for data integration, quality, governance, lineage, metadata, and control. AI teams gain trusted, contextual enterprise data for building Generative AI applications, copilots, and intelligent AI agents. Why Enterprises Choose SCIKIQ AI-ready in 3–6 weeks | 200+ connectors | 99.9% availability | No-code | Multi-cloud | No vendor lock-in | No replatforming SCIKIQ has production deployments across industries including manufacturing, retail, aviation, logistics, BFSI, healthcare, and other data-intensive enterprises.

BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Gemini in BigQuery offers AI-driven tools for assistance and collaboration, such as code suggestions, visual data preparation, and smart recommendations designed to boost efficiency and reduce costs. BigQuery delivers an integrated platform featuring SQL, a notebook, and a natural language-based canvas interface, catering to data professionals with varying coding expertise. This unified workspace streamlines the entire analytics process.

AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack. At its core is the AnalyticsCreator Governed Control Model, which keeps business meaning, data structures, transformation rules, dependencies and technical implementation connected in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design. Generated assets can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns. Changes made in the model can be propagated across dependent project assets, while lineage, documentation and impact analysis remain connected to the underlying design. This helps teams reduce repetitive engineering work, standardise delivery and understand the effect of change before regenerating affected assets. AnalyticsCreator generates native Microsoft technology rather than requiring a proprietary runtime. Organisations retain ownership of the resulting implementation and can integrate generated assets into existing Git, Azure DevOps and CI/CD processes. Design Intelligence extends this governed project context into AI-assisted data engineering by giving authorised AI tools and agents structured access to metadata, lineage, dependencies and design rules. Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery, SAP-to-Microsoft analytics architectures and repeatable data product engineering.