Best Data Pipeline Software for Databricks Data Intelligence Platform

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.

Gathr is a Data+AI fabric, helping enterprises rapidly deliver production-ready data and AI products. Data+AI fabric enables teams to effortlessly acquire, process, and harness data, leverage AI services to generate intelligence, and build consumer applications— all with unparalleled speed, scale, and confidence. Gathr’s self-service, AI-assisted, and collaborative approach enables data and AI leaders to achieve massive productivity gains by empowering their existing teams to deliver more valuable work in less time. With complete ownership and control over data and AI, flexibility and agility to experiment and innovate on an ongoing basis, and proven reliable performance at real-world scale, Gathr allows them to confidently accelerate POVs to production. Additionally, Gathr supports both cloud and air-gapped deployments, making it the ideal choice for diverse enterprise needs. Gathr, recognized by leading analysts like Gartner and Forrester, is a go-to-partner for Fortune 500 companies, such as United, Kroger, Philips, Truist, and many others.

Apache Kafka®, is an open-source distributed streaming platform.

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows that result in a saving of ~10 hours of engineering time/week and 10x faster reporting, analytics, and decision making. The platform supports 100+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 500 data-driven companies spread across 35+ countries trust Hevo for their data integration needs.

Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud. Key Features: Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines. Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance. Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects. Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.

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.

Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.

Streamkap is a modern streaming ETL platform built on top of Apache Kafka and Flink, designed to replace batch ETL with streaming in minutes. It enables data movement with sub-second latency using change data capture for minimal impact on source databases and real-time updates. The platform offers dozens of pre-built, no-code source connectors, automated schema drift handling, updates, data normalization, and high-performance CDC for efficient and low-impact data movement. Streaming transformations power faster, cheaper, and richer data pipelines, supporting Python and SQL transformations for common use cases like hashing, masking, aggregations, joins, and unnesting JSON. Streamkap allows users to connect data sources and move data to target destinations with an automated, reliable, and scalable data movement platform. It supports a broad range of event and database sources.

Data teams can collaborate as software engineering teams by using version control, quality assurance, documentation, and modularity. Analytics errors should be treated as serious as production product bugs. Analytic workflows are often manual. We believe that workflows should be designed to be executed with one command. Data teams use dbt for codifying business logic and making it available to the entire organization. This is useful for reporting, ML modeling and operational workflows. Built-in CI/CD ensures data model changes are made in the correct order through development, staging, production, and production environments. dbt Cloud offers guaranteed uptime and custom SLAs.

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.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

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.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

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.

Capture your unstructured data to meet all of your LLM requirements. Datavolo replaces point-to-point, single-use code with flexible, reusable, fast pipelines. This allows you to focus on the most important thing, which is doing amazing work. Datavolo gives you an edge in the competitive market. Get unrestricted access to your data, even the unstructured files on which LLMs depend, and boost your generative AI. You can create pipelines that will grow with you in minutes and not days. Configure instantly from any source to any location at any time. {Trust your data because lineage is built into every pipeline.|Every pipeline includes a lineage.} Pipelines and configurations that are only used once can be a thing of past. Datavolo is a powerful tool that uses Apache NiFi to harness unstructured information and unlock AI innovation. Our founders have dedicated their lives to helping organizations get the most out of their data.

Unravel makes data available anywhere: Azure, AWS and GCP, or in your own datacenter. Optimizing performance, troubleshooting, and cost control are all possible with Unravel. Unravel allows you to monitor, manage and improve your data pipelines on-premises and in the cloud. This will help you drive better performance in the applications that support your business. Get a single view of all your data stack. Unravel gathers performance data from every platform and system. Then, Unravel uses agentless technologies to model your data pipelines end-to-end. Analyze, correlate, and explore all of your cloud and modern data. Unravel's data models reveal dependencies, issues and opportunities. They also reveal how apps and resources have been used, and what's working. You don't need to monitor performance. Instead, you can quickly troubleshoot issues and resolve them. AI-powered recommendations can be used to automate performance improvements, lower cost, and prepare.

Data-driven organizations are constantly striving to become more data-driven. They build dashboards, analytics and data pipelines throughout the modern data stack. Unfortunately, data reliability issues are a major problem for most organizations, leading to poor decisions and a lack of trust in the data as an organisation, which directly impacts their bottom line. Resolving complex issues is a time-consuming and manual process that involves multiple teams, all of whom rely on tribal knowledge. They manually reverse-engineer complex data pipelines across various platforms to identify the root-cause and to understand the impact. Pantomath, a data pipeline traceability and observability platform, automates data operations. It continuously monitors datasets across the enterprise data ecosystem, providing context to complex data pipes by creating automated cross platform technical pipeline lineage.

Tarsal is infinitely scalable, so as your company grows, Tarsal will grow with you. Tarsal allows you to easily switch from SIEM data to data lake data with just one click. Keep your SIEM, and migrate analytics to a data-lake gradually. Tarsal doesn't require you to remove anything. Some analytics won't work on your SIEM. Tarsal can be used to query data in a data lake. Your SIEM is a major line item in your budget. Tarsal can be used to send some of this data to your data lake. Tarsal is a highly scalable ETL pipeline designed for security teams. With just a few mouse clicks you can easily exfiltrate terabytes with instant normalization and route the data to your destination.

Our automated data operations platform allows businesses to reduce the number of full-time staff needed to support data operations. Our product simplifies and reduces costs for a process that is usually very manual and costly. Most companies are too busy processing data to pay attention to its quality. Our product will make you proactive in the area of data quality. Our platform, which has a built-in system of alerts and clear visibility over all incoming data, ensures that you meet your data quality standards. Our platform is a unique solution that combines many manual processes into one platform. After a simple install and a few configurations, the BettrData.io Platform is ready for use.