January 2025 Summaries

CData announced its sponsorship and participation in the Gartner Data & Analytics Summit 2025, held March 3–5 at the Gaylord Palms Resort and Conference Center in Orlando, where it will exhibit at booth #932 and offer scheduled 30-minute meetings. The company will discuss how modern data connectivity can help organizations address the growing complexity of SaaS environments, noting that large enterprises use more than 660 SaaS applications on average and add roughly 11 new applications every 30 days. CData positions robust integration and connectivity as essential for faster operations, better decision-making, and preparation for AI initiatives, while highlighting its inclusion in Gartner’s 2024 Magic Quadrant for Data Integration Tools and its Strong Performer placement in the 2024 Gartner Peer Insights Voice of the Customer report.

CData Connect AI plans to introduce scheduled queries at the end of January, enabling users to automatically run Query Builder or custom SQL queries from the Data Explorer and write results to selected destinations at defined intervals. The feature is intended to support low-maintenance data workflows without requiring custom scripts or complex pipeline infrastructure, helping users keep reporting, analytics, and project-management data current. Supported destinations include BigQuery, Databricks, PostgreSQL, Redshift, Smartsheet, and Snowflake, while the platform’s broad source connectivity allows data to be consolidated and delivered across systems. Users create or write a query, choose a schedule and destination, and Connect AI executes the data write automatically; the capability will be available to all users, with a free trial offered through the Data Explorer.

Data masking is a security technique that modifies sensitive information such as personal, financial, healthcare, and proprietary data so it remains useful for analytics, testing, development, and sharing without revealing original values. Unlike encryption, masking obscures data directly and may be applied statically to non-production copies, dynamically at query time according to user permissions, during data transfers, or through deterministic and randomized approaches. Common techniques include shuffling values, nulling fields, substituting realistic fictitious data, and tokenizing information through secure token vaults. Organizations use masking to reduce breach impact, meet regulations such as GDPR, HIPAA, and PCI DSS, support secure third-party sharing, limit insider access, protect cloud migrations, and provide realistic test data. The text also presents CData Connect AI as a platform for governed live-data access, role-based permissions, compliance support, and secure sharing across SaaS, cloud, and on-premises systems.

Retailers such as Amazon, Walmart, Nordstrom, and Costco have used data infrastructure and predictive analytics to improve demand forecasting, supply chains, marketing decisions, and customer experiences, illustrated by Amazon’s response to the pandemic-era toilet paper shortage. The passage argues that many organizations still struggle to unify data across expanding mixes of legacy, cloud, on-premises, and vendor-specific systems, limiting their ability to support analytics and artificial intelligence. It identifies three core requirements for modern retail data operations: ecosystem-agnostic architecture that connects disparate systems, a central governed data-access layer that makes information available to analytics teams, and automated data engineering that reduces manual work and technical debt. Examples involving Office Depot and BJ’s Wholesale are presented to show how cross-platform integration and automated data preparation can improve performance, business continuity, workforce analysis, and retention.

CData’s Foundations conference highlighted how Google BigQuery, UiPath, APOS Systems, Klipfolio, and Stellar One embed CData’s connector library to broaden data access, reduce the burden of building and maintaining integrations, and support new product capabilities. Google described connectors as a way to unlock enterprise and legacy data for transformation, reporting, and generative AI, noting that reliable AI depends on accessible, high-quality data. UiPath uses CData to extract high-volume process data from systems such as SAP, Salesforce, and ServiceNow for its Process Mining product, with roughly 75% of its customers licensing the technology. APOS integrated CData JDBC drivers into its SAP Analytics Cloud gateway to provide live access to non-SAP sources, while Klipfolio replaced a resource-intensive in-house connector effort with a white-labeled, scalable connectivity layer for self-service analytics. Stellar One adopted CData’s hosted Connect AI platform to simplify data migration during Acumatica ERP implementations, illustrating how outsourced connectivity can help smaller providers address complex integration needs.

ETL, or extract, transform, load, is a data integration process that gathers information from sources such as databases, APIs, files, and cloud services, cleans and restructures it to meet business and analytical requirements, and loads it into centralized destinations including data warehouses, cloud storage, and analytics platforms. It supports reliable reporting, business intelligence, machine learning, cloud migrations, IoT analytics, database replication, and industry-specific applications by improving data accessibility, quality, security, scalability, and operational efficiency while reducing manual work, errors, and storage costs. Organizations can run ETL in batch, streaming, or incremental modes and select from tools such as CData Sync, Airbyte, Apache Airflow, AWS Glue, Azure Data Factory, Google Cloud Dataflow, Informatica, Matillion, Microsoft SSIS, Talend, and others based on their infrastructure and integration needs. CData Sync is presented as a platform offering prebuilt connectors and real-time synchronization between on-premises and cloud systems to help create analysis-ready data pipelines.

Hybrid integration platforms (HIPs) and integration platforms as a service (iPaaS) are two approaches for connecting the growing number of business systems that collect and exchange operational data. HIPs support both on-premises and cloud deployments, making them suitable for organizations that must integrate legacy infrastructure with cloud applications; they offer flexible deployment, centralized architecture, security, and agility, but can require greater technical expertise, more complex tooling, and higher planning costs for maintenance and scaling. iPaaS platforms are cloud-first services designed primarily for cloud-to-cloud integration, providing lower upfront costs, rapid deployment, prebuilt connectors, simplified administration, and easier scalability, although they have limited support for on-premises data transformation and connectivity and may face performance bottlenecks with large or complex workloads. Choosing between them depends on an organization’s existing systems, integration complexity, IT skills, budget, and growth plans: HIP is generally better for hybrid environments with substantial on-premises requirements, while iPaaS is better suited to cloud-focused businesses seeking a scalable, lower-maintenance integration solution.

Structured data is organized into predefined formats such as rows and columns, enabling efficient storage, querying, and analysis in relational databases for uses including financial reporting, inventory management, customer relationship management, website analytics, and transaction processing. Unstructured data, which accounts for an estimated 80% of generated data, includes emails, social media posts, videos, audio, reviews, and other raw content that does not fit fixed schemas and is typically stored in scalable data lakes or distributed systems. While structured data can be readily analyzed with SQL and business intelligence tools, unstructured data requires techniques such as machine learning, natural language processing, and video analytics to extract insights. Both types support business decision-making: structured data is suited to well-defined operational questions, while unstructured data can reveal customer sentiment, market shifts, buying preferences, and product-development opportunities.

CData’s 2024.2 Driver and Connector release expands connectivity with new integrations for Salesforce Data Cloud, which unifies customer data across Salesforce and external systems, and PingOne, enabling analysis of identity and access-management data. The update includes breaking changes driven by vendor API revisions for Marketo, affecting most functionality, and Amazon Marketplace, requiring updates for users of several inbound-shipment tables, views, and stored procedures. Performance and usability improvements include up to tenfold faster SQL Server linked servers through the CData SQL Gateway, ARM64 support for Power BI Connectors on Windows 11 virtual machines, Veeva Vault CRM support, and additional SaaS interface URLs in query results. Numerous source-specific additions cover functions, authentication, data upload, reporting, change tracking, new tables and views, API updates, and faster query performance across platforms including Airtable, Amazon Athena, Confluence, Databricks, DocuSign, Dynamics 365, GitHub, SharePoint, ServiceNow, and YouTube Analytics.

At the inaugural CData Foundations conference, enterprise data leaders discussed how centralized, automated access to organizational data can support better decision-making, operational efficiency, and AI adoption. Speakers emphasized eliminating data silos and simplifying data preparation, with examples including Repligen’s seven-figure annual savings from supply chain and inventory analytics, faster realization of value from its Snowflake investment, and improved revenue forecasting. Bayer reported cost and staffing reductions by automating pharmacovigilance data integration, while Healthsource Distributors said self-service integration contributed to 40% revenue growth through stronger supply chain connections and market expansion. The event also highlighted CData’s partnership with Srinsoft as a combination of self-service technology and implementation support. Unity Health Toronto illustrated how a secure, governed data infrastructure can enable AI in regulated healthcare settings while addressing privacy, compliance, and accuracy requirements.

Operational databases and data warehouses serve complementary but distinct roles in data management: operational databases support real-time, day-to-day transactions, while data warehouses organize historical information for analytics and strategic decision-making. Operational systems use OLTP, row-oriented storage, normalized tables, and ACID transaction controls to efficiently process frequent inserts, updates, and simple queries for applications such as e-commerce, CRM, point-of-sale, and logistics. Data warehouses use OLAP, column-oriented storage, denormalized star or snowflake schemas, and scheduled batch updates to support complex queries, reporting, trend analysis, marketing analytics, and financial forecasting. The appropriate choice depends on whether an organization needs live operational data or scalable historical analysis, though many businesses use both systems together and integrate data sources through tools such as CData Sync.

Apache Iceberg and Delta Lake are open table formats that add structure, governance, scalability, and reliable data management capabilities to data lakes, supporting analytics, AI/ML, and large-scale processing workloads. Iceberg, created by Netflix and maintained as an Apache project, emphasizes multi-engine compatibility, scalable Parquet-based metadata, hidden partitioning, schema evolution, atomic operations, and efficient handling of large, read-heavy or batch-oriented datasets. Delta Lake, initiated by Databricks, is closely integrated with Apache Spark and provides ACID transactions, transaction-log metadata, file compaction, indexing, and time travel, making it particularly suitable for write-intensive, real-time, streaming, machine learning, and data warehousing applications. Both formats provide versioning, rollback or historical query capabilities, and storage optimization services, but Iceberg is positioned as more flexible across varied engines and cloud infrastructures, while Delta Lake is especially advantageous in Spark- and Databricks-centered environments requiring strong consistency during frequent updates. Selecting between them depends on data consistency needs, existing tools, workload patterns, retention requirements, and desired ecosystem flexibility, while platforms such as CData Connect AI aim to provide standardized access across these and other data lake environments.

CData Software has updated its Drivers and Connectors for Veeva Vault to support Vault CRM through Veeva Vault API version 24.2 and later, enabling users to access the life sciences CRM platform from BI, ETL, AI/ML, and custom applications. Vault CRM unifies sales, marketing, medical, and service data around a single customer database to support personalized healthcare professional engagement, operational efficiency, and patient outcomes. The drivers expose Vault CRM objects and documents as relational-style views that can be queried with standard SQL, while supporting dynamic schema detection, multiple authentication methods, REST API and VQL access, and document operations through stored procedures. They integrate with technologies including JDBC, ODBC, ADO.NET, Python, Power BI, Tableau, Excel, SSIS, and Google Spreadsheets, supporting analytics, data integration, and warehousing use cases involving platforms such as Informatica, Talend, MuleSoft, BigQuery, Snowflake, Redshift, and Azure SQL Server. Users install the appropriate connector, configure their Vault CRM URL and credentials, and can then query data similarly to a conventional database.

Structured data is information organized into predefined formats, typically rows and columns in databases or spreadsheets, that makes it easy to store, search, query, analyze, and automate with tools such as SQL and business intelligence platforms. Its consistent schema, relational links, and defined fields support efficient reporting, forecasting, AI and machine learning applications, and operational systems such as customer relationship management, financial transaction processing, reservations, inventory management, marketing analytics, and electronic health records. Common tools include relational databases such as MySQL, PostgreSQL, and SQLite, along with OLAP platforms and spreadsheets. Compared with unstructured data such as videos and social media posts, and semi-structured formats such as JSON and XML, structured data offers greater accessibility, storage efficiency, scalability, and analytical simplicity, but its rigid schemas can limit flexibility and make adaptation to changing requirements or dynamic data types costly.

CData Foundations 2024’s Data Architecture Track featured leaders from PartnerRe, WashTec, and NYU discussing how modern data platforms can improve collaboration, agility, governance, and AI readiness. PartnerRe emphasized breaking down business and IT silos through shared rules, democratized data access, and a scalable platform supporting compliance and analytics. WashTec described using a data mesh approach and robust infrastructure to let teams experiment and pursue AI initiatives without managing excessive technical complexity. NYU shared its incremental five-year adoption of data virtualization to unify distributed institutional data, build stakeholder alignment, and improve data literacy. Across the sessions, speakers identified accessible, governed, scalable, and reusable data products as essential foundations for timely insights and meaningful AI-driven innovation.

CData has introduced drivers and connectors for Salesforce Data Cloud that enable live integration with more than 200 BI, analytics, AI, reporting, and data integration tools, reducing the technical complexity of accessing the platform through its API. Salesforce Data Cloud consolidates Salesforce and external SaaS, CRM, marketing, support, and database data to support customer 360 initiatives and deeper analysis. The new offerings include JDBC, ODBC, and ADO.NET connectivity, native connectors for Excel, Tableau, and Power BI, plus Python and PowerShell options, with CData Sync and CData Connect AI planned for future releases. They support SQL-92 queries, several authentication methods including OAuth and JWT, and access to Calculated Insights, Data Graphs, Data Lake Objects, and Data Model Objects. A Power BI example illustrates installation, OAuth-based connection setup, and use of DirectQuery to select and visualize Salesforce Data Cloud data without coding.

CData’s 2024 recap highlights its growth and expanding role in data connectivity as organizations increased AI adoption and sought more effective ways to manage data. Key developments included recognition in Gartner’s Magic Quadrant for Data Integration Tools as the only new vendor listed, the launch of its Foundations virtual conference, and the introduction of CData Connect Spreadsheets, a free tool for connecting live data to Excel and Google Sheets. The company also secured $350 million in funding from Warburg Pincus and Accel, earned awards for its software solutions, data virtualization technology, customer satisfaction, workplace culture, and growth, and appeared on the Inc. 5000 and Deloitte Technology Fast 500 lists. CData attributes these achievements to its customers, partners, and employees and plans to use the momentum to further develop its products and support evolving customer needs in 2025.

Data virtualization provides a unified virtual layer for accessing and querying diverse data sources without physically moving or duplicating information, offering an alternative to traditional ETL-based integration. It supports AI and machine learning by reducing data silos and latency, combining real-time data from disparate systems, simplifying preparation, and providing the diverse, current datasets needed for training and inference. Applications include supply-chain optimization, customer segmentation, fraud detection, predictive maintenance, and retail inventory management, where integrated data can improve forecasting, personalization, anomaly detection, and operational decisions. Effective implementation requires assessing existing data systems, selecting an appropriate platform, maintaining governance and security controls, optimizing performance, and training teams. CData Connect AI is presented as a platform that provides governed, real-time access to SaaS, cloud, and on-premises data without replication.