Best Data Engineering Tools for Cloud - Page 3

Privacy engineering is an emerging field that has intersections with data engineering, information security, software development, and privacy law. Its goal is to ensure that personal data is stored and processed in a legally compliant way that respects and protects the privacy of the individuals this data belongs in the best possible way. Security engineering is on one hand a requirement for privacy engineering but also an independent discipline that aims to guarantee the secure processing and storage of sensitive data in general. If your organization processes data that is either sensitive or personal (or both), you need privacy & security engineering. This is especially true if you do your own data engineering or data science.

Roseman Labs enables you to encrypt, link, and analyze multiple data sets while safeguarding the privacy and commercial sensitivity of the actual data. This allows you to combine data sets from several parties, analyze them, and get the insights you need to optimize your processes. Tap into the unused potential of your data. With Roseman Labs, you have the power of cryptography at your fingertips through the simplicity of Python. Encrypting sensitive data allows you to analyze it while safeguarding privacy, protecting commercial sensitivity, and adhering to GDPR regulations. Generate insights from personal or commercially sensitive information, with enhanced GDPR compliance. Ensure data privacy with state-of-the-art encryption. Roseman Labs allows you to link data sets from several parties. By analyzing the combined data, you'll be able to discover which records appear in several data sets, allowing for new patterns to emerge.

Xtract Data Automation Suite (XDAS) is a comprehensive platform designed to streamline process automation for data-intensive workflows. It offers a vast library of over 300 pre-built micro solutions and AI agents, enabling businesses to design and orchestrate AI-driven workflows with no code environment, thereby enhancing operational efficiency and accelerating digital transformation. Key components of XDAS include Bot Studio, which allows users to create custom bots and scripts; Scrape Studio, for effortless web data extraction; GenAI Studio, for developing AI agents that process unstructured data; HITL Studio, which integrates human oversight into data workflows; and XRAG Studio, for building advanced AI systems using retrieval-augmented generation techniques. By leveraging these tools, XDAS helps businesses ensure compliance, reduce time to market, enhance data accuracy, and forecast market trends across various industries.

SplineCloud is an open knowledge management platform designed to facilitate the discovery, formalization, and exchange of structured and reusable knowledge in science and engineering. It enables users to organize data into structured repositories, making it findable and accessible. The platform offers tools such as an online plot digitizer for extracting data from graphs and an interactive curve fitting tool that allows users to define functional relationships in datasets using smooth spline functions. Users can also reuse datasets and relations in their models and calculations by accessing them directly through the SplineCloud API or by utilizing open source client libraries for Python and MATLAB. The platform supports the development of reusable engineering and analytical applications, aiming to reduce redundancy in design processes, preserve expert knowledge, and facilitate better decision-making.

​TensorStax is an AI-powered platform that automates data engineering tasks, enabling businesses to efficiently manage data pipelines, database migrations, ETL/ELT processes, and data ingestion within their cloud infrastructure. Its autonomous agents integrate seamlessly with existing tools like Airflow and dbt, facilitating end-to-end pipeline development and proactive issue detection to minimize downtime. Deployed within a company's Virtual Private Cloud (VPC), TensorStax ensures data security and privacy. By automating complex data workflows, it allows teams to focus on strategic analysis and decision-making. ​

Ingest, prepare, and process data pipelines at scale for AI and analytics in the cloud. Informatica’s comprehensive data engineering portfolio provides everything you need to process and prepare big data engineering workloads to fuel AI and analytics: robust data integration, data quality, streaming, masking, and data preparation capabilities. Rapidly build intelligent data pipelines with CLAIRE®-powered automation, including automatic change data capture (CDC) Ingest thousands of databases and millions of files, and streaming events. Accelerate time-to-value ROI with self-service access to trusted, high-quality data. Get unbiased, real-world insights on Informatica data engineering solutions from peers you trust. Reference architectures for sustainable data engineering solutions. AI-powered data engineering in the cloud delivers the trusted, high quality data your analysts and data scientists need to transform business.

Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization.

Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable.

We Make Data for the World's Most Valuable Companies Innodata solves your toughest data engineering challenges using artificial intelligence and human expertise. Innodata provides the services and solutions you need to harness digital data at scale and drive digital disruption in your industry. We securely and efficiently collect & label your most complex and sensitive data, delivering near-100% accurate ground truth for AI and ML models. Our easy-to-use API ingests your unstructured data (such as contracts and medical records) and generates normalized, schema-compliant structured XML for your downstream applications and analytics. We ensure that your mission-critical databases are accurate and always up-to-date.