Best Data Management Software for Google Cloud Dataproc

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

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

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

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.

Discover insights to boost sales growth with code-free, fully automated data pipelines to data lakes and cloud warehouses. Flexible, standards-based platform that unifies sales and marketing data to automate insights and smarter growth. Say goodbye to manual data downloads that are expensive and messy. You will always know exactly what you'll be charged and only pay what you actually use. Access to data-ready data is a great way to fuel your tools. We only work with official APIs as certified developers. Data pipelines from well-known sources are easy to use. These data pipelines are pre-built, pre-transformed and ready to go. Unlock data from Amazon Vendor Central and Amazon Seller Central, Instagram Stories. Teams can quickly and economically realize the value of their data with code-free data ingestion and transformation. Databricks, Amazon Redshift and other trusted data destinations like Databricks or Amazon Redshift ensure that data is always protected.

Google Cloud Dataplex is a data fabric that allows organizations to centrally manage, monitor and govern data across data lakes and data warehouses. It provides access to trusted data, while enabling analytics and AI to be powered at scale. Dataplex provides a unified data management interface that allows users to automate data discovery and classification, as well as metadata enrichment, for structured, semistructured, or unstructured data in Google Cloud, and beyond. It simplifies data curation, tiering and archiving by facilitating the logical organization into business-specific domains, using lakes and zones. Centralized governance and security features allow for policy management, monitoring and auditing across silos of data, allowing distributed data ownership and global oversight. Dataplex also has built-in capabilities for data quality and data lineage, automating data assessments and capturing lineage.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system.

Cloud Composer's managed nature with Apache Airflow compatibility allow you to focus on authoring and scheduling your workflows, rather than provisioning resources. Google Cloud products include BigQuery, Dataflow and Dataproc. They also offer integration with Cloud Storage, Cloud Storage, Pub/Sub and AI Platform. This allows users to fully orchestrate their pipeline. You can schedule, author, and monitor all aspects of your workflows using one orchestration tool. This is true regardless of whether your pipeline lives on-premises or in multiple clouds. You can make it easier to move to the cloud, or maintain a hybrid environment with workflows that cross over between the public cloud and on-premises. To create a unified environment, you can create workflows that connect data processing and services across cloud platforms.

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.

The Collibra Data Intelligence Cloud offers a best-in class catalog, flexible governance and continuous quality. It also has built-in privacy. A best-in-class data catalogue that supports your users includes embedded governance, privacy, and quality. You can raise the bar by ensuring that teams can quickly access, understand, and access data from all sources, including business applications and data science tools, in one central location. Your data deserves privacy. Automate, centralize and guide workflows to encourage collaboration and operationalize privacy. Collibra Data Lineage gives you the complete story about your data. Automatically map relationships between applications, systems, and reports to provide a context rich view of the enterprise. Focus on the data that you are most concerned about and make sure it is accurate, complete, and trustworthy.

Monitor your data health, and monitor your pipeline performance. Get unified visibility for all pipelines that use cloud-native tools such as Apache Spark, Snowflake and BigQuery. A platform for Data Engineers that provides observability. Data engineering is becoming more complex as business stakeholders demand it. Databand can help you catch-up. More pipelines, more complexity. Data engineers are working with more complex infrastructure and pushing for faster release speeds. It is more difficult to understand why a process failed, why it is running late, and how changes impact the quality of data outputs. Data consumers are frustrated by inconsistent results, model performance, delays in data delivery, and other issues. A lack of transparency and trust in data delivery can lead to confusion about the exact source of the data. Pipeline logs, data quality metrics, and errors are all captured and stored in separate, isolated systems.

Open source data governance suite to manage data lakes and databases. Tokern is an easy-to-use toolkit for collecting, organizing and analysing metadata from data lakes. Runs as a command-line application for quick tasks. Run as a service to continuously collect metadata. Use reporting dashboards to analyze lineage, access control, and PII data. Or programmatically in Jupyter notebooks. Tokern is an open-source data governance suite for data lakes and databases. You can improve the ROI of your data, comply to regulations like HIPAA, CCPA, and GDPR, and protect your data from insider threats with confidence. Centralized metadata management for users, jobs, and datasets. Other data governance features are powered by this feature. Track column-level data lineage for Snowflake and AWS Redshift. You can build lineage using query history or ETL scripts. Interactive graphs and programming with APIs and SDKs allow you to explore lineage.

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.

You can monitor and control all the actions of your data pipelines without changing any code. Monitor data and pipelines to prevent downtime proactively and quickly identify root causes. Optimize pipeline runs, job performance and cost to maintain SLAs and save money. Accelerate platform upgrades and code deployments while maintaining reliability and performance. Data & Performance checks in line with pipelines. Checking input data before pipelines are even run. Preemption of runs by automatic means. Definity eliminates the need to build end-to-end coverage so that you are protected in every dimension. Definity shifts observability into post-production in order to achieve ubiquity and increase coverage while reducing manual effort. Definity agents run automatically with every pipeline and leave no footprint. Every data asset can be viewed in a single view, including pipelines, infrastructure, lineage and code. Avoid async checking by detecting in-run. Preempt all runs, including inputs.