Alternatives to Chalk

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

Fivetran is the smartest method to replicate data into your warehouse. Our zero-maintenance pipeline is the only one that allows for a quick setup. It takes months of development to create this system. Our connectors connect data from multiple databases and applications to one central location, allowing analysts to gain profound insights into their business.

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

Your offline data can be used to make real-time predictions, without the need for custom pipelines. Data consistency is achieved between offline training and online prediction, eliminating train-serve bias. Standardize data engineering workflows within a consistent framework. Feast is used by teams to build their internal ML platforms. Feast doesn't require dedicated infrastructure to be deployed and managed. Feast reuses existing infrastructure and creates new resources as needed. You don't want a managed solution, and you are happy to manage your own implementation. Feast is supported by engineers who can help with its implementation and management. You are looking to build pipelines that convert raw data into features and integrate with another system. You have specific requirements and want to use an open-source solution.

Composable is an enterprise-grade DataOps platform designed for business users who want to build data-driven products and create data intelligence solutions. It can be used to design data-driven products that leverage disparate data sources, live streams, and event data, regardless of their format or structure. Composable offers a user-friendly, intuitive dataflow visual editor, built-in services that facilitate data engineering, as well as a composable architecture which allows abstraction and integration of any analytical or software approach. It is the best integrated development environment for discovering, managing, transforming, and analysing enterprise data.

Datuum is an AI-powered data integration tool that offers a unique solution for organizations looking to streamline their data integration process. With our pre-trained AI engine, Datuum simplifies customer data onboarding by allowing for automated integration from various sources without coding. This reduces data preparation time and helps establish resilient connectors, ultimately freeing up time for organizations to focus on generating insights and improving the customer experience. At Datuum, we have over 40 years of experience in data management and operations, and we've incorporated our expertise into the core of our product. Our platform is designed to address the critical challenges faced by data engineers and managers while being accessible and user-friendly for non-technical specialists. By reducing up to 80% of the time typically spent on data-related tasks, Datuum can help organizations optimize their data management processes and achieve more efficient outcomes.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

K2View believes that every enterprise should be able to leverage its data to become as disruptive and agile as possible. We enable this through our Data Product Platform, which creates and manages a trusted dataset for every business entity – on demand, in real time. The dataset is always in sync with its sources, adapts to changes on the fly, and is instantly accessible to any authorized data consumer. We fuel operational use cases, including customer 360, data masking, test data management, data migration, and legacy application modernization – to deliver business outcomes at half the time and cost of other alternatives.

Kestra is a free, open-source orchestrator based on events that simplifies data operations while improving collaboration between engineers and users. Kestra brings Infrastructure as Code to data pipelines. This allows you to build reliable workflows with confidence. The declarative YAML interface allows anyone who wants to benefit from analytics to participate in the creation of the data pipeline. The UI automatically updates the YAML definition whenever you make changes to a work flow via the UI or an API call. The orchestration logic can be defined in code declaratively, even if certain workflow components are modified.

GlassFlow is an event-driven, serverless data pipeline platform for Python developers. It allows users to build real time data pipelines, without the need for complex infrastructure such as Kafka or Flink. GlassFlow is a platform that allows developers to define data transformations by writing Python functions. GlassFlow manages all the infrastructure, including auto-scaling and low latency. Through its Python SDK, the platform can be integrated with a variety of data sources and destinations including Google Pub/Sub and AWS Kinesis. GlassFlow offers a low-code interface that allows users to quickly create and deploy pipelines. It also has features like serverless function executions, real-time connections to APIs, alerting and reprocessing abilities, etc. The platform is designed for Python developers to make it easier to create and manage event-driven data pipes.

Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.

Advanced Analytics Software Platform that Helps Sales, Marketing and Technology Leaders Make Great Business Decisions Based On Powerful Data-Driven Information. This software addresses the wide range of AI requirements across data infrastructure, data engineering, and data analytics. Each client's unique requirements are met with customized UI, workflows, and proprietary algorithms. Modular components allow for custom configurations. This component supports, augments, and automates decision-making. Better business results are possible by eliminating human biases. The adoption rate of AI is unprecedented. Leading companies need a rapid and scaleable implementation strategy to stay competitive. These four capabilities can be combined to create a scalable business impact.

Dataplane's goal is to make it faster and easier to create a data mesh. It has robust data pipelines and automated workflows that can be used by businesses and teams of any size. Dataplane is more user-friendly and places a greater emphasis on performance, security, resilience, and scaling.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.

Switchboard, a data engineering automation platform that is driven by business teams, allows you to aggregate disparate data at scale and make better business decisions. Get timely insights and precise forecasts. No more outdated manual reports or poorly designed pivot tables that don’t scale. Directly pull data from the right formats and reconfigure them in a non-code environment. Reduce dependency on engineering teams. API outages, bad schemas and missing data are gone thanks to automatic monitoring and backfilling. It's not a dumb API. Instead, it's an ecosystem of pre-built connectors which can be quickly and easily adapted to transform raw data into strategic assets. Our team of experts have worked in data teams at Google, Facebook, and other companies. These best practices have been automated to improve your data game. Data engineering automation platform that enables authoring and workflow processes. It is designed to scale with terabytes.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

Molecula, an enterprise feature store, simplifies, speeds up, and controls big-data access to power machine scale analytics and AI. Continuously extracting features and reducing the data dimensionality at the source allows for millisecond queries, computations, and feature re-use across formats without copying or moving any raw data. The Molecula feature storage provides data engineers, data scientists and application developers with a single point of access to help them move from reporting and explaining with human scale data to predicting and prescribing business outcomes. Enterprises spend a lot of time preparing, aggregating and making multiple copies of their data before they can make any decisions with it. Molecula offers a new paradigm for continuous, real time data analysis that can be used for all mission-critical applications.

Prefect Cloud is a command centre for your workflows. You can instantly deploy from Prefect core to gain full control and oversight. Cloud's beautiful UI allows you to keep an eye on your infrastructure's health. You can stream real-time state updates and logs, launch new runs, and get critical information right when you need it. Prefect Cloud's managed orchestration ensures that your code and data are safe while Prefect Cloud's Hybrid Model keeps everything running smoothly. Cloud scheduler runs asynchronously to ensure that your runs start on the right time every time. Advanced scheduling options allow you to schedule parameter values changes and the execution environment for each run. You can set up custom actions and notifications when your workflows change. You can monitor the health of all agents connected through your cloud instance and receive custom notifications when an agent goes offline.

Today there is a constant buzz about how top companies are using big data to gain competitive advantage. Your company is trying to be one of these market-leading companies. The reality is that more than 80% of big-data projects fail to go to production. This is because project implementation can be complex and resource-intensive. It can take months, if not years, to complete. The technology is complex and the people with the right skills are difficult to find. Automates all data workflows, from source to consumption. Automates the migration of data and workloads between legacy Data Warehouse systems and big data platforms. Automates the orchestration and management complex data pipelines in production. Alternative methods, such as custom development or stitching together multiple points solutions, are more expensive, inflexible and time-consuming, and require specialized skills to assemble.

witboost allows your company to become data-driven, reduce time-to market, it expenditures, and overheads by using a modular, scalable and efficient data management system. There are a number of modules that make up witboost. These modules are building blocks that can be used as standalone solutions to solve a specific problem or to create the ideal data management system for your company. Each module enhances a specific function of data engineering and can be combined to provide the perfect solution for your specific needs. This will ensure a fast and seamless implementation and reduce time-to market, time-to value and, consequently, the TCO of your data engineering infrastructure. Smart Cities require digital twins to anticipate needs and avoid unforeseen issues, gather data from thousands of sources, and manage telematics that is ever more complicated.

DatErica: Revolutionizing Data Processing DatErica, a cutting edge data processing platform, automates and streamlines data operations. It provides scalable, flexible solutions to complex data requirements by leveraging a robust technology stack that includes Node.js. The platform provides advanced ETL capabilities and seamless data integration across multiple sources. It also offers secure data warehousing. DatErica’s AI-powered tools allow sophisticated data transformation and verification, ensuring accuracy. Users can make informed decisions with real-time analytics and customizable dashboards. The user-friendly interface simplifies the workflow management while real-time monitoring, alerts and notifications enhance operational efficiency. DatErica is perfect for data engineers, IT teams and businesses that want to optimize their data processes.

No more low-level code or gluing together complex systems. SQL makes it easy to build and deploy pipelines quickly. Data engineering service that allows developers and data engineers to quickly build and deploy data pipelines for data-driven apps. It is easy to connect to and find available data using pre-built connectors for messaging, storage, and database engines. Each connection you make will result in a stream of data to or from the system. You can create your pipelines using SQL with Decodable. Pipelines use streams to send and receive data to and from your connections. Streams can be used to connect pipelines to perform the most difficult processing tasks. To ensure data flows smoothly, monitor your pipelines. Create curated streams that can be used by other teams. To prevent data loss due to system failures, you should establish retention policies for streams. You can monitor real-time performance and health metrics to see if everything is working.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

AI teams are faced with challenges that require new technologies. These technologies are built by us. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. AI and software development go hand in hand. Built with data scientists, ML experts, and data engineers at heart. Don't reinvent your wheel! Production is fast and cost-effective. All your data is stored by you. Your machines are used to train your models. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. New technologies are required for AI teams. These technologies are built by us. Studio is an extension to BitBucket, GitLab, and GitHub. Register for the online SaaS version, or contact us to start an on-premise installation

Ask On Data is an open source Data Engineering/ ETL software that uses chat-based AI. Ask On Data, with its agentic capabilities and next-gen data stack pioneering technology, can help create data pipelines through a simple chat interface. It can be used to perform tasks such as Data Migration, Data Loading and Data Transformations. It also allows for Data Cleaning, Data Wrangling and Data Analysis. Data Scientists can use this tool to get clean data. Data Analysts and BI Engineers can create calculated tables. Data Engineers will also be able to use this tool in order to increase their efficiency.

The machine learning platform that unifies data and orchestrates intelligent behavior among revenue-generating teams. It also delivers out-of the-box attribution and actionable analytics. QFlow.ai processes the gigabytes worth of data that Salesforce.com's activity table stores. To help you win more deals and generate more opportunities, we normalize, trend, or analyze your sales efforts. QFlow.ai uses data engineering for outbound activity reporting. It focuses on one crucial factor: whether they were productive. It also automatically displays critical metrics such as average days between first activity and opp creation, and average days between opp création to close. To understand trends in sales activity and productivity over time, Sales Effort data can either be filtered by a team or an individual.

RudderStack is the smart customer information pipeline. You can easily build pipelines that connect your entire customer data stack. Then, make them smarter by pulling data from your data warehouse to trigger enrichment in customer tools for identity sewing and other advanced uses cases. Start building smarter customer data pipelines today.

Mage transforms data into predictions. In minutes, you can build, train, then deploy predictive models. No AI experience necessary. You can increase user engagement by ranking content in your user's homefeed. Conversion can be increased by showing users the most relevant products to purchase. You can predict which users will quit using your app to increase retention. Matching users in a marketplace can increase conversion. Data is the most crucial part of building AI. Mage will help you navigate this process and offer suggestions on how to improve data. You will become an AI expert. AI and its predictions can be confusing. Mage will explain every metric in detail, showing you how your AI model thinks. With just a few lines code, you can get real-time predictions. Mage makes it easy to integrate your AI model into any application.

Talend Pipeline designer is a self-service web application that transforms raw data into analytics-ready data. Create reusable pipelines for extracting, improving, and transforming data from virtually any source. Then, pass it on to your choice of destination data warehouses, where you can use it as the basis for dashboards that drive your business insights. Create and deploy data pipelines faster. With an easy visual interface, you can design and preview batch or streaming data directly in your browser. Scale your hybrid and multi-cloud technology with native support and improve productivity through real-time development. Live preview allows you to visually diagnose problems with your data. Documentation, quality assurance, and promotion of datasets will help you make better decisions faster. Transform data to improve data quality using built-in functions that can be applied across batch or stream pipelines. Data health becomes an automated discipline.

Unified stream and batch data processing that is serverless, fast, cost-effective, and low-cost. Fully managed data processing service. Automated provisioning of and management of processing resource. Horizontal autoscaling worker resources to maximize resource use Apache Beam SDK is an open-source platform for community-driven innovation. Reliable, consistent processing that works exactly once. Streaming data analytics at lightning speed Dataflow allows for faster, simpler streaming data pipeline development and lower data latency. Dataflow's serverless approach eliminates the operational overhead associated with data engineering workloads. Dataflow allows teams to concentrate on programming and not managing server clusters. Dataflow's serverless approach eliminates operational overhead from data engineering workloads, allowing teams to concentrate on programming and not managing server clusters. Dataflow automates provisioning, management, and utilization of processing resources to minimize latency.

Amazon Managed Workflows (MWAA), a managed orchestration service that allows Apache Airflow to create and manage data pipelines in the cloud at scale, is called Amazon Managed Workflows. Apache Airflow is an open source tool that allows you to programmatically create, schedule, and monitor a series of processes and tasks, also known as "workflows". Managed Workflows lets you use Airflow and Python to create workflows and not have to manage the infrastructure for scalability availability and security. Managed Workflows automatically scales the workflow execution to meet your requirements. It is also integrated with AWS security services, which allows you to have fast and secure access.

Lightbend technology allows developers to quickly build data-centric applications that can handle the most complex, distributed applications and streaming data streams. Lightbend is used by companies around the world to address the problems of distributed, real-time data to support their most important business initiatives. Akka Platform is a platform that makes it easy for businesses build, deploy, manage, and maintain large-scale applications that support digitally transformational initiatives. Reactive microservices are a way to accelerate time-to-value, reduce infrastructure costs, and lower cloud costs. They take full advantage the distributed nature cloud and are highly efficient, resilient to failure, and able to operate at any scale. Native support for encryption, data destruction, TLS enforcement and compliance with GDPR. Framework to quickly build, deploy and manage streaming data pipelines.

In a developer-friendly visual editor, you can design, debug, run, and troubleshoot data jobflows and data transformations. You can orchestrate data tasks that require a specific sequence and organize multiple systems using the transparency of visual workflows. Easy deployment of data workloads into an enterprise runtime environment. Cloud or on-premise. Data can be made available to applications, people, and storage through a single platform. You can manage all your data workloads and related processes from one platform. No task is too difficult. CloverDX was built on years of experience in large enterprise projects. Open architecture that is user-friendly and flexible allows you to package and hide complexity for developers. You can manage the entire lifecycle for a data pipeline, from design, deployment, evolution, and testing. Our in-house customer success teams will help you get things done quickly.

Integrate sensors to IoT applications and enrich sensor data by integrating control system and environmental data. This data can be integrated with enterprise data in real-time and used to develop predictive algorithms that uncover new insights and harvest data for meaningful purposes. Analytics can be used to predict maintenance problems, analyze asset utilization, reduce defects, and optimize processes. Remote monitoring and diagnostics services can be provided by using the power of connected devices. IoT Analytics can be used to predict safety hazards and comply to regulations to reduce workplace accidents.

Data is essential to improve the efficiency, profitability, and experience of streaming video. Datazoom allows video publishers to manage distributed architectures more efficiently by centralizing, standardizing and integrating data in real time. This creates a more powerful data pipeline, improves observability and adaptability, as well as optimizing solutions. Datazoom is a video data platform which continuously gathers data from endpoints such as a CDN or video player through an ecosystem of collectors. Once the data has been gathered, it is normalized with standardized data definitions. The data is then sent via available connectors to analytics platforms such as Google BigQuery, Google Analytics and Splunk. It can be visualized using tools like Looker or Superset. Datazoom is your key for a more efficient and effective data pipeline. Get the data you need right away. Do not wait to get your data if you have an urgent issue.

Cloud Foundry and Kubernetes support microservice-based streaming and batch processing. Spring Cloud Data Flow allows you to create complex topologies that can be used for streaming and batch data pipelines. The data pipelines are made up of Spring Boot apps that were built using the Spring Cloud Stream and Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a variety of data processing use cases including ETL, import/export, event streaming and predictive analytics. Spring Cloud Data Flow server uses Spring Cloud Deployer to deploy data pipelines made from Spring Cloud Stream and Spring Cloud Task applications onto modern platforms like Cloud Foundry or Kubernetes. Pre-built stream and task/batch starter applications for different data integration and processing scenarios allow for experimentation and learning. You can create custom stream and task apps that target different middleware or services using the Spring Boot programming model.

Event Hubs is a fully managed, real time data ingestion service that is simple, reliable, and scalable. Stream millions of events per minute from any source to create dynamic data pipelines that can be used to respond to business problems. Use the geo-disaster recovery or geo-replication features to continue processing data in emergencies. Integrate seamlessly with Azure services to unlock valuable insights. You can allow existing Apache Kafka clients to talk to Event Hubs with no code changes. This allows you to have a managed Kafka experience, without the need to manage your own clusters. You can experience real-time data input and microbatching in the same stream. Instead of worrying about infrastructure management, focus on gaining insights from your data. Real-time big data pipelines are built to address business challenges immediately.

BDB is a modern data analysis and BI platform that can dig deep into your data to uncover actionable insights. It can be deployed on-premise or in the cloud. Our unique microservices-based architecture includes elements such as Data Preparation and Predictive, Pipeline, Dashboard designer, and Pipeline. This allows us to offer customized solutions and scalable analysis to different industries. BDB's NLP-based search allows users to access the data power on desktop, tablet, and mobile. BDB is equipped with many data connectors that allow it to connect to a variety of data sources, apps, third-party API's, IoT and social media. It works in real-time. It allows you to connect to RDBMS and Big data, FTP/ SFTP Server flat files, web services, and FTP/ SFTP Server. You can manage unstructured, semi-structured, and structured data. Get started on your journey to advanced analysis today.

You can deploy production-ready change data capture pipes for high-volume, real time data replication without writing a single line code. Supercharged Change Data Capture. Arcion's distributed Change Data Capture, CDC, allows for automatic schema conversion, flexible deployment, end-to-end replication and much more. Arcion's zero-data loss architecture ensures end-to-end consistency and built-in checkpointing. You can forget about performance and scalability concerns with a distributed, highly parallel architecture that supports 10x faster data replication. Arcion Cloud is the only fully managed CDC offering. You'll enjoy autoscaling, high availability, monitoring console and more. Reduce downtime and simplify data pipelines architecture.

Data-driven computational pipelines. Nextflow allows for reproducible and scalable scientific workflows by using software containers. It allows adaptation of scripts written in most common scripting languages. Fluent DSL makes it easy to implement and deploy complex reactive and parallel workflows on clusters and clouds. Nextflow was built on the belief that Linux is the lingua Franca of data science. Nextflow makes it easier to create a computational pipeline that can be used to combine many tasks. You can reuse existing scripts and tools. Additionally, you don't have to learn a new language to use Nextflow. Nextflow supports Docker, Singularity and other containers technology. This, together with integration of the GitHub Code-sharing Platform, allows you write self-contained pipes, manage versions, reproduce any configuration quickly, and allow you to integrate the GitHub code-sharing portal. Nextflow acts as an abstraction layer between the logic of your pipeline and its execution layer.

Pitchly is more than just a data platform. We help you make the most of it. Our integrated warehouse-to worker process brings business data to life. We go beyond other enterprise data platforms. Content production is a key part of the future of work. Repeatable content can be made more accurate and faster by switching to data-driven production. Workers are then free to do higher-value work. Pitchly gives you the power to create data-driven content. You can set up brand templates and build your workflow. Then, you can enjoy on-demand publishing with the reliability of data-driven accuracy and consistency. You can manage all your assets in one content library, including tombstones, case studies and bios as well as reports and any other content assets Pitchly clients produce.

It is difficult, costly, and risky to connect systems to scale AI projects. Pandio's cloud native managed solution simplifies data pipelines to harness AI's power. You can access your data from any location at any time to query, analyze, or drive to insight. Big data analytics without the high cost Enable data movement seamlessly. Streaming, queuing, and pub-sub with unparalleled throughput, latency and durability. In less than 30 minutes, you can design, train, deploy, and test machine learning models locally. Accelerate your journey to ML and democratize it across your organization. It doesn't take months or years of disappointment. Pandio's AI driven architecture automatically orchestrates all your models, data and ML tools. Pandio can be integrated with your existing stack to help you accelerate your ML efforts. Orchestrate your messages and models across your organization.

Many components are required to build real-time apps or services. These components include Kafka and VPC hosting, infrastructure code, container orchestration and observability. The Quix platform handles all the moving parts. Connect your data and get started building. That's it. There are no provisioning clusters nor configuring resources. You can use Quix connectors for ingesting transaction messages from your financial processing system in a virtual private clouds or on-premise data centers. For security and efficiency, all data in transit is encrypted from the beginning and compressed using Protobuf and G-Zip. Machine learning models and rule-based algorithms can detect fraudulent patterns. You can display fraud warning messages in support dashboards or as troubleshooting tickets.

Open source solutions that require no maintenance can save you time and money. Your engineers will enjoy working with data because it is integrated, managed and highly reliable. DoubleCloud offers a range of managed open-source services, or you can leverage the full platform's power, including data storage and visualization, orchestration, ELT and real-time visualisation. We offer leading open-source solutions like ClickHouse Kafka and Airflow with deployments on Amazon Web Services and Google Cloud. Our no-code ELT allows real-time data sync between systems. It is fast, serverless and seamlessly integrated into your existing infrastructure. Our managed open-source data visualisation allows you to visualize your data in real time by creating charts and dashboards. Our platform is designed to make engineers' lives easier.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

Bodo's powerful parallel computing engine and powerful compute engine provide efficient execution and effective scaling, even for 10,000+ cores or PBs of data. Bodo makes it easier to develop and maintain data science, data engineering, and ML workloads using standard Python APIs such as Pandas. End-to-end compilation prevents frequent failures and catches errors before they reach production. With Python's simplicity, you can experiment faster with large datasets from your laptop. Produce production-ready code without having to refactor for scaling large infrastructure.

Ascend provides data teams with a unified platform that allows them to ingest and transform their data and create and manage their analytics engineering and data engineering workloads. Ascend is supported by DataAware intelligence. Ascend works in the background to ensure data integrity and optimize data workloads, which can reduce maintenance time by up to 90%. Ascend's multilingual flex-code interface allows you to use SQL, Java, Scala, and Python interchangeably. Quickly view data lineage and data profiles, job logs, system health, system health, and other important workload metrics at a glance. Ascend provides native connections to a growing number of data sources using our Flex-Code data connectors.

Aggua is an AI platform with augmented data fabric that gives data and business teams access to their data. It creates Trust and provides practical Data Insights for a more holistic and data-centric decision making. With just a few clicks, you can find out what's happening under the hood of your data stack. You can access data lineage, cost insights and documentation without interrupting your data engineer's day. With automated lineage, data engineers and architects can spend less time manually tracing what data type changes will break in their data pipelines, tables, and infrastructure.

Data analysts can now gain insights faster and without any barriers. Intelligent data stack automation, gain insights from your data 10x quicker with AI. Intelligence for the modern data-stack has arrived, a technology that was developed at Stanford's AI lab and is now available to enterprises. Use natural language to extract value from your messy data, complex and siloed in minutes. Tell your data what you want and it will generate code to execute. Automate complex data tasks in a way that is specific to your company and not covered by templated solutions. Automate data-intensive workflows using the modern data stack. Discover insights in minutes and not months. Uniquely designed and tuned to your organization's requirements. Snowflake, Databricks Redshift, BigQuery and more are integrated with dbt.