We created the following reviews after conducting an in-depth briefing with each vendor. Our analysis focuses on each product’s key differentiators and is designed to help companies create a short list of products to evaluate. We feel this is the appropriate role of a research firm rather than to rank and rate vendors, which is a slippery slope for many reasons.
Acceldata
CEO Rohit Choudhary, CTO Ashwin Rajeev, Raghu Mandikonda, and Gaurav Nagar co-founded Acceldata in 2018 because they saw large, data-driven enterprises losing control of complex analytics projects. Pipelines broke, jobs failed, data got corrupted, and data teams often couldn’t find and fix the issue in time to prevent business disruption. Choudhary and the founding team seek to expose and optimize the intricate workings of the modern data stack, from data stores and processors to pipelines and analytics tools, so that enterprises can improve data quality, pipeline performance, and cost. Acceldata raised $46 million, grew to over 150 employees, and amassed enterprise customers such as Oracle, Pratt & Whitney, Verisk, and Dun & Bradstreet. Acceldata appeals to large enterprises that seek to reduce the risk of ambitious data initiatives across heterogeneous hybrid and multi-cloud environments.
Among vendors profiled here, Acceldata addresses the widest range of use cases for data platforms such as Snowflake and Databricks, or data lakes based on Hadoop. It helps business and IT leaders handle SLAs, chargeback, and business process design; it also helps data engineers and architects improve data quality, pipeline performance, and architectural design. In addition, platform and site reliability engineers can use Acceldata to streamline infrastructure and plan capacity needs. For example, data engineers can track and adjust how job schedulers, Spark processors, and object stores interact with one another in a data lake and check data quality indicators such as value ranges, schemas, and lineage. Armed with this information, data engineers can both manage current workloads and help platform teams design better architectures for the future.
Demo
youtu.be/CKbT5Au4ulY
Anomalo
Anomalo CEO Elliot Shmukler and CTO Jeremy Stanley found a mutual appreciation for data quality as they struggled with quality issues while working at Instacart. They founded Anomalo in 2018 to provide deep data inspection for cloud data warehouses such as Snowflake and Databricks. Anomalo helps data teams find and fix data issues with an automated, ML-driven solution that creates its own rules and thresholds. Shmukler and Stanley raised $33 million, added more than 30 employees, and won customers that include Discover, Buzzfeed, and the AirBnB alternative Home to Go. Anomalo integrates with a wide range of cloud data warehouses, catalogs, and workflow tools. It best serves enterprises with mature analytics initiatives that cannot afford to miss even small data errors.
Anomalo differentiates itself by offering what it calls “deep observability,” which focuses on data accuracy, consistency, and utility, in addition to the “foundational observability” that just tracks data delivery status and timeliness. Anomalo helps data engineers, data scientists, analysts, and even business owners monitor and visualize data quality through a common GUI. For example, they can manipulate rich visualizations to identify, inspect, and assess the business impact of a data change. Anomalo also automates the root cause analysis to isolate and remediate issues such as null values, broken links, or missing attributes. Anomalo serves a range of traditional and cloud-native enterprises.
Bigeye
Bigeye CEO Kyle Kirwan and CTO Egor Gryaznov experienced first-hand the challenges of ensuring data quality while working at Uber, whose entire business model depends on accurate, real-time data. They founded Bigeye in 2019 to give data scientists (Kyle’s heritage) and data engineers (Egor’s heritage) a single platform that optimizes data quality for data modernization and advanced analytics projects. They raised $66 million, grew to more than 50 employees, and now serve thousands of data users each week. Bigeye proposes that “data reliability” represents the single greatest obstacle—and opportunity—for enterprise data initiatives. It best serves enterprises that seek a best-of-breed tool for data quality observability, focused on cloud data warehouses, such as Snowflake, Amazon Redshift, and Databricks, and transactional databases such as MySQL, SQL Server, and PostgreSQL.
Data teams use Bigeye to optimize data quality for BI, machine learning, migrations, and ongoing data ingestion. Bigeye scans many metrics, both using its library of default metrics and integrating with custom enterprise checks, to spot subtle anomalies that might have a big business impact. When it spots those anomalies, Bigeye helps speed resolution with root cause analysis and in-line debugging, and sends alerts and recommended actions to users via notification platforms such as Slack. It also offers a command-line interface and YAML-based “Bigconfig” system that automates the implementation of observability rules across enterprise environments. Bigeye has native integration with tools such as Alation to assist cataloging and dbt to assist transformations, as well as REST APIs that provide open access to other tools. Cloud-first and cloud-native enterprises such as Zoom, Instacart, and Udacity rely on Bigeye for data quality observability.
Demo
youtu.be/XuxloZ4Sb_A
Data Kitchen
Chris Bergh, Gil Benghiat, and Eric Estabrooks lived the pain of poor data delivery as they struggled to purge data errors from their prior company’s analytics services. In 2013 Bergh and team founded Data Kitchen, a bootstrapped venture, to help companies fix such issues with a DataOps platform based on total quality management and statistical process control. Data Kitchen pioneered many aspects of the discipline of DataOps, which seeks to make data pipelines more efficient and effective with CI/CD, pipeline orchestration, and data observability. It now espouses an “observability first” approach to DataOps, helping enterprises understand the health and status of existing data flows before building new pipelines.
Data Kitchen offers both data pipeline observability and data quality observability across what it calls the “data journey,” from inception to transformation, delivery, and consumption. Enterprises use Data Kitchen to reduce the risk of their hybrid data environments by finding and fixing issues before they reach the business owners and applications that consume data. Data Kitchen monitors data pipelines in development and production, and enforces user-configured rules to take action when pipeline jobs or data quality fall short of expectations.
Data Kitchen also monitors the outputs of operations observability tools such as DataDog to help data teams understand the performance and utilization of compute clusters and other architectural elements that support pipelines. Data Kitchen customers include AstraZeneca, Bristol Meyers Squibb, and Catholic Relief Services.
FirstEigen
FirstEigen carves a distinct niche. While other data observability vendors tend to implement standard data quality checks, FirstEigen helps enterprises implement hundreds or thousands of table-specific rules for inspecting operational data in hybrid environments, data lakes in particular. CEO Seth Rao and CTO Angsuman Dutta founded FirstEigen in 2015 and run it as a bootstrapped venture. Their DataBuck product, released in 2017, helps financial institutions and other regulation-sensitive enterprises automate rule creation and application, as well as root cause analysis. Data engineers, governance professionals, and business domain experts use DataBuck to reduce the downtime, effort, and cost of managing data issues.
FirstEigen automatically generates data quality rules—reducing the need for manual scripting—by creating “fingerprints” for individual tables that describe the relationship between data values and expectations for each column. Each fingerprint has various rules that users approve, deny, or change, then put to work. While many data observability tools layer onto cloud data warehouses, FirstEigen can insert rules and checks at multiple points in the pipeline, from source to target. FirstEigen integrates with a wide ecosystem of cloud data platforms as well as Alation, which incorporates FirstEigen’s quality scores into its catalog. Unlike many competitors, FirstEigen also partners with systems integrators such as Infosys, Cognizant, and Deloitte.
Demo
youtu.be/ZPqnTZiwOFA
IBM Databand
Four years ago, CEO Josh Benamran, CTO Evgeny Shulman, and chief product officer Victor Shafran banded together to launch Databand. Their mission: give data engineering and platform teams a better way to fix broken ETL pipelines—addressing data quality and pipeline performance—while delivering data products. Databand helps improve data flows for AI/ML models and data applications by improving the mean time to detection and resolution of data incidents. Databand gained quick traction and sold itself to IBM in June 2022, becoming part of a massive portfolio with related offerings such as a data fabric, operations observability, and model observability. Databand is best suited for cloud-first enterprises whose competitive advantage depends on custom software, complex modeling, and heavy investment in the modern data stack.
Databand differentiates itself by inspecting data in motion to support advanced use cases for data science and product teams. For example, Trax uses Databand to help some of the world’s largest brands and retailers study shopper actions at store shelves. Databand reduced incidents on their pipelines from 60% to less than 1% to support Trax’s neural network for deep learning. This enabled Trax to deploy more ML models, support more customers, and improve compute efficiency. Other customers include Oracle, FanDuel, and the online travel agency Agoda. Databand positions its product as a standalone, modular component of the IBM data fabric portfolio that integrates with popular tools like Airflow, Spark, dbt Labs, and other complementary IBM offerings for data fabric and operations/model observability.
1:1 Interview with Sponsor
youtu.be/qr-4JMTPD4g
Demo
youtu.be/lmiLSgnptBs
DataOps.live
CEO Justin Mullen and CTO Guy Adams co-founded London-based DataOps.live in 2020 as a software spinoff from their data consulting business. Their stated mission: “to make data products and teams as governed and agile as their software counterparts,” with an initial focus on the Snowflake Data Cloud. They also helped shape the philosophy of the TrueDataOps community. DataOps.live differentiates itself by providing a control layer that orchestrates, monitors, and controls various third-party tools and custom pipeline elements. It best serves mid-sized and large enterprises that want to improve productivity with a unified developer and operator experience on Snowlake. Other cloud data platform vendors are on their near-term roadmap.
Modern data environments rely on a broad ecosystem of tools. To help manage data flows into Snowflake, DataOps.live integrates with and orchestrates ingestion tools such as Fivetran and Stitch, transformation tools such as Matillion and dbt, and data quality observability tools such as Monte Carlo and Soda. To help manage data within and across data pipelines, it integrates with catalogs such as Collibra and data.world, notebooks such as Jupyter, AI/ML platforms such as Dataiku, and data access management tools such as Okera and Immuta. The DataOps.live platform enables data engineers and data product managers to configure, build, test, and release data pipelines and data products that comprise these various elements.
DataOps.live offers multiple levels of observability for data pipelines and data products—including pipeline performance, data quality, data usage, and infrastructure cost—by studying trends and anomalies in metadata. To assess the value and viability of a data product, users can refer to a knowledge graph that illustrates the interrelationships of metadata such as source file names, quality scores, and query performance. DataOps.live’s new release, now in private preview, supports “Data Products” as complex objects, traces their lineage, and shares their metadata with tools such as Monte Carlo and Collibra.
Informatica
The largest and most mature vendor profiled here, Informatica offers data observability tools as part of a comprehensive portfolio for data management and governance. The Redwood City, CA-based company started as an ETL provider in 1993. It grew organically and via acquisition to address critical segments such as cataloging, DataOps, data privacy, and master data management, all integrated as modules on an AI-driven platform. Informatica has a long history in data quality observability, focused on structured data at rest and data in motion. It recently extended its offerings to address data pipeline observability, in particular to help enterprises operationalize and scale AI/ML, while controlling the cost of data delivery as they move to the cloud. Various Informatica products already gather relevant metadata, making data observability a natural add-on. All these capabilities are offered as cloud-native services of Informatica’s Intelligent Data Management Cloud.
Many enterprise stakeholders, including data engineers, DataOps engineers, and business owners, use Informatica’s graphical interface and AI-guided prompts for data observability. They study three layers: infrastructure, data pipelines, and business consumption. At the infrastructure layer, Informatica monitors elements such as compute clusters, spots performance issues, and suggests how to fix them. It also helps measure, predict, and control the compute cost of individual jobs that pipelines perform. At the pipeline layer, Informatica profiles data, detects anomalies, and helps remediate issues. At the business layer, Informatica tracks consumption by user, dataset, and consumption to assist compliance with internal policies and external regulations.
Enterprises such as Amgen and Discount Tire rely on Informatica for data observability. It makes about $1.5 billion in annual revenue, has more than 5,000 customers, and trades on the New York Stock Exchange.
Monte Carlo
Monte Carlo, the youngest startup profiled here, already has more than 150 customers and $236 million in funding. Not bad for CEO Barr Moses, who describes herself as a late adopter of technology. Moses and CTO Lior Gavish co-founded the company in 2019 after asking the question: why are there so many tools to make infrastructure reliable, and so few to make data reliable? Both traditional and cloud-native enterprises embrace their tool to minimize business disruptions by scouring high volumes of cloud data for quality issues and automating workflows to fix what they find.
Monte Carlo improves the productivity of data engineers and stewards, and accelerates data adoption across organizations in several ways. To find data quality issues, it detects anomalies via machine learning. To resolve incidents, it automatically surfaces potential root causes, for example by tracking lineage across a wide ecosystem of cloud data lakes, data warehouses, orchestration tools, and workflow notification systems. Unlike many competitors, they also integrate with business intelligence (BI) tools and data lake environments to help data engineers triage issues based on user and business impact. In
addition, Monte Carlo insights help prevent the delivery of bad data, for example with automated “circuit breakers” that halt pipelines when data fails a quality test, as well as scenario modeling that predicts future damage from potential issues. Enterprises such as CNN, JetBlue, and Hootsuite rely on Monte Carlo to reduce data downtime in their cloud environments.
Soda
Maarten Masschelein and Tom Baeyens, CEO and CTO respectively, founded Soda to offer enterprises a collaborative, self-service approach to improving data quality. They raised funding from European venture capitalists and angel investors such as the founders of US-based Collibra and Looker. Soda software addresses two use cases. First, it enables data producers and consumers to maintain quality agreements for data products. Second, it enables data engineers to test the quality of data both in flight and at rest, using intuitive open-source commands that they and their colleagues can easily understand. Soda integrates with an ecosystem that includes the Apache Kafka streaming platform, cloud data platforms such as Snowflake, Databricks, and Amazon Redshift, and on-premise systems such as Microsoft SQL Server, IBM db2, and PostgreSQL. It also integrates with the data transformation tool dbt and data catalogs such as Alation and Metaphor, and offers webhooks to assist integration with other tools.
Soda differentiates the Soda Cloud enterprise offering with ease of use and self-service. It offers prescriptive workflows that help stakeholders collaborate to identify, understand, and resolve data quality issues. Soda’s open-source framework includes the Soda Checks Language (SodaCL), a YAML-based language that enables data engineers and data analysts alike to build their quality agreements. Collaborating on these rules and consistently checking data quality helps to prevent issues that could have a negative downstream impact on business decisions.
Soda has raised $15 million and earned the trust of new customers such as the meal-kit provider HelloFresh, online course platform Udemy, and Brazilian retailer Americanas.
Unravel
CEO Kunal Agarwal and CTO Shivnath Babu founded Unravel in 2016 to automate performance troubleshooting for Big Data pipelines that rely on data lake elements such as Apache Spark. Since then the Unravel team has raised more than $107 million and grown to more than 100 employees. They also broadened their portfolio to address much more than data pipeline performance. Unravel offers “DataOps observability” that assists each stage of the iterative data lifecycle. This includes planning, deployment, operation, monitoring, orchestration, troubleshooting, and optimization. Their solution best serves enterprises that need to improve cross-functional collaboration between data-related roles such as data engineers, data analysts, and data scientists; and business roles such as product and platform owners, operations managers, and chief data or analytics officers.
As part of this strategy, Unravel addresses multiple categories of observability beyond just data pipeline observability. This includes operations observability, which spots and fixes performance issues with data-driven applications or infrastructure elements such as the processing engines and compute clusters that support them. It includes FinOps observability, which offers budget planning dashboards, chargeback reports, alerts, and AI-enabled optimization recommendations to control cloud costs by user, application, or infrastructure element. Unravel also now addresses data quality observability through external integration, starting with the open-source platform Great Expectations. Marquee enterprise customers such as Intel, HSBC, and Kimberly-Clark use Unravel to help their data teams optimize data workloads across their stack.
Demo
youtu.be/YI0IJF5SySE