# Best Big Data Analytics Software

## How Many Big Data Analytics Software Products Does G2 Track?

**Total Products under this Category:** 110

### Category Stats (Aug 2026)

- **Average Rating:** 4.46/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Datacoves (+0.27%) - Among all products in this category, Datacoves recorded the largest rating increase compared to last month

_Last updated: August 07, 2026_

## How Does G2 Rank Big Data Analytics Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 8,400+ Authentic Reviews
- 110+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for Big Data Analytics Software
 ![G2 Grid® for Big Data Analytics Software plotting products by satisfaction and market presence](https://www.g2.com/categories/big-data-analytics/grids.png?focus%5B%5D=10470&focus%5B%5D=6073&focus%5B%5D=10938&focus%5B%5D=1308796&focus%5B%5D=989&focus%5B%5D=67962&focus%5B%5D=27024&focus%5B%5D=52199)

Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, IBM watsonx.data, Alteryx, Azure Databricks, Kyvos Semantic Layer, and Azure Synapse Analytics.

Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-analytics/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=alteryx&focus%5B%5D=azure-databricks&focus%5B%5D=kyvos-semantic-layer&focus%5B%5D=azure-synapse-analytics)

**Sponsored**

### Housecall Pro

Housecall Pro is a comprehensive business solution designed specifically for home service professionals, providing a suite of innovative tools and features within an easy-to-use platform. Trusted by over 200k+ Pros, Housecall Pro aims to streamline operations for professionals in various home service industries, including plumbing, electrical work, landscaping, and cleaning services. This platform is accessible via both web and mobile applications, allowing users to manage their businesses efficiently from anywhere. The target audience for Housecall Pro includes small to medium-sized businesses in the home services sector that are looking to enhance their operational efficiency and increase revenue. The platform caters to a variety of use cases, such as automating marketing efforts to attract new customers, facilitating online booking, and managing job schedules. By integrating these functionalities, Housecall Pro empowers service professionals to focus more on their core work while effectively managing the business side of operations. Key features of Housecall Pro include automated marketing campaigns that help users reach potential clients, visually appealing proposal creation, and consumer financing options that enable businesses to secure larger jobs. The platform also supports online payment processing, allowing customers to pay via multiple methods, including credit cards, bank transfers, and mobile wallets. This flexibility not only enhances customer satisfaction but also accelerates cash flow for service providers. In addition to revenue growth and payment solutions, Housecall Pro offers robust job management capabilities. Users can automate routine tasks such as scheduling, dispatching, and invoicing, while also tracking leads and job progress through a comprehensive workflow management board. Real-time alerts enhance communication among team members and clients, ensuring everyone stays informed throughout the service process. Furthermore, the platform integrates seamlessly with third-party tools like QuickBooks, enabling users to sync data effortlessly and manage payroll and employee benefits. Housecall Pro also provides valuable insights through detailed analytics and reporting on key business metrics, allowing professionals to make informed decisions and scale their operations effectively. On average, users report a more than 35% increase in monthly revenue after their first year with Housecall Pro. Subscribers gain access to an online community where they can connect with other home service professionals, sharing insights and best practices. This collaborative environment fosters growth and learning, making Housecall Pro not just a tool, but a partner in business success.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=1041&secure%5Bchosen_at%5D=2026-08-08T02%3A59%3A55Z&secure%5Bdisplayable_resource_id%5D=394&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=retargeted_product&secure%5Bplacement_resource_ids%5D%5B%5D=14037&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=14037&secure%5Bresource_id%5D=1041&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fbig-data-analytics&secure%5Btoken%5D=468a462660f04f358bc2e7616a721024d2ec3399adc7ea8f509d4e39136bca2d&secure%5Burl%5D=https%3A%2F%2Fwww.housecallpro.com%2Ftop%2Fhome-service-software%2F%3Futm_source%3DG2%26utm_medium%3Dpaid-external-page%26utm_campaign%3Daq_ga_field_service_management_cpc%26mc%3DG2%26msc%3Daq_ga_field_service_management_cpc&secure%5Burl_type%5D=free_trial)

### [Databricks](https://www.g2.com/products/databricks/reviews)

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,334

#### How Do G2 Users Rate Databricks?

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 9.0/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.9/10 (Category avg: 8.5/10)
- **Data Workflow:** 8.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Databricks?

- **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc)
- **Company Website:** databricks.com
- **Year Founded:** 2013
- **HQ Location:** San Francisco, CA
- **Twitter:** @databricks  
92,269 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bddca64732f61b923d96364e8c8eb35711aab4f98797cb00ab071ff24fbdd392&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3477522%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,627 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 47% Large, 38% Medium

#### What Do G2 Reviewers Say About Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications.
- Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services.
- Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency.
- Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights.
- Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization.

##### Cons

- Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability.
- Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects.
- Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools.
- Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes.
- Users face **complex setup** challenges initially, though support helps simplify the experience over time.

#### What Are Recent G2 Reviews of Databricks?

**["Reliable Platform for Building Scalable Data Pipelines"](https://www.g2.com/survey_responses/databricks-review-13198355)**

**Rating:** 5.0/5.0 stars

_— aravind k._

[Read full review](https://www.g2.com/survey_responses/databricks-review-13198355)

**["Databricks Streamlines ETL and Analytics with Scalable Notebooks"](https://www.g2.com/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

[Read full review](https://www.g2.com/survey_responses/databricks-review-13181721)

#### What Are G2 Users Discussing About Databricks?

- [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes
- [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

### [Google Cloud BigQuery](https://www.g2.com/de/products/google-cloud-bigquery/reviews)

BigQuery ist ein KI-bereites, petabyte-skalierbares und kosteneffizientes Data Warehouse, das es Ihnen ermöglicht, Analysen über riesige Datenmengen nahezu in Echtzeit durchzuführen. Speichern Sie 10 GiB Daten und führen Sie bis zu 1 TiB Abfragen pro Monat kostenlos aus.

**Average Rating:** 4.5/5.0

**Total Reviews:** 1,145

#### How Do G2 Users Rate Google Cloud BigQuery?

- **War the product ein guter Geschäftspartner?:** 8.6/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 8.7/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.8/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 8.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Google Cloud BigQuery?

- **Verkäufer:** [Google](https://www.g2.com/de/sellers/google)
- **Gründungsjahr:** 1998
- **Hauptsitz:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Dateningenieur, Datenanalyst
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 38% Large, 35% Medium

#### What Do G2 Reviewers Say About Google Cloud BigQuery?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Benutzerfreundlichkeit** von Google Cloud BigQuery, die eine schnelle Analyse massiver Datensätze ohne Aufwand ermöglicht.
- Benutzer schätzen die **außergewöhnliche Geschwindigkeit** von BigQuery, die eine schnelle Verarbeitung großer Datensätze nahtlos ermöglicht.
- Benutzer lieben die **einfache Integration** mit Google Cloud-Diensten, die eine reibungslose Datenanalyse und -verwaltung ermöglicht.
- Benutzer schätzen die **schnellen Abfragefähigkeiten** von Google Cloud BigQuery, die eine mühelose Analyse massiver Datensätze ermöglichen.
- Benutzer schätzen die **Abfrageeffizienz** von BigQuery, das mühelos komplexe Abfragen auf riesigen Datensätzen mit Geschwindigkeit verarbeitet.

##### Cons

- Benutzer finden, dass die **Kosten mit Google Cloud BigQuery schnell eskalieren können** , was eine sorgfältige Abfrageoptimierung erfordert, um die Ausgaben zu verwalten.
- Benutzer haben Schwierigkeiten mit **Abfrageproblemen** in BigQuery, da sie mit steigenden Kosten und Herausforderungen bei der Abfrageoptimierung und Fehlersuche konfrontiert sind.
- Benutzer finden **Kostenmanagement herausfordernd** mit Google Cloud BigQuery aufgrund unvorhersehbarer Preisgestaltung und Vorfällen unerwarteter Gebühren.
- Benutzer erleben **Kostenprobleme** mit Google Cloud BigQuery und kämpfen mit unerwartet hohen Rechnungen und eingeschränkter Preistransparenz.
- Benutzer finden die **steile Lernkurve** von Google Cloud BigQuery herausfordernd, insbesondere bei fortgeschrittenen Funktionen und Optimierungstechniken.

#### What Are Recent G2 Reviews of Google Cloud BigQuery?

**["Einfach zu bedienendes Cloud-Tool mit teilbaren, gespeicherten Abfragen"](https://www.g2.com/de/survey_responses/google-cloud-bigquery-review-12958418)**

**Rating:** 4.0/5.0 stars

_— Reetika P._

[Read full review](https://www.g2.com/de/survey_responses/google-cloud-bigquery-review-12958418)

**["Skalierbares, sicheres BigQuery, das nahtlos über Dienste hinweg verbindet"](https://www.g2.com/de/survey_responses/google-cloud-bigquery-review-12638747)**

**Rating:** 5.0/5.0 stars

_— Aayush M._

[Read full review](https://www.g2.com/de/survey_responses/google-cloud-bigquery-review-12638747)

#### What Are G2 Users Discussing About Google Cloud BigQuery?

- [Is Big Query free?](https://www.g2.com/de/discussions/is-big-query-free) - 3 comments, 1 upvote
- [Is BigQuery part of Google Cloud Platform?](https://www.g2.com/de/discussions/is-bigquery-part-of-google-cloud-platform) - 2 comments, 2 upvotes
- [Worauf basiert Google BigQuery?](https://www.g2.com/de/discussions/what-is-google-bigquery-based-on) - 1 comment
- [Wofür wird Google BigQuery verwendet?](https://www.g2.com/de/discussions/what-is-google-bigquery-used-for) - 1 comment

### [Snowflake](https://www.g2.com/fr/products/snowflake/reviews)

Snowflake permet à chaque organisation de mobiliser leurs données avec le AI Data Cloud de Snowflake. Les clients utilisent le AI Data Cloud pour unir des données cloisonnées, découvrir et partager des données en toute sécurité, alimenter des applications de données et exécuter divers charges de travail d'IA/ML et d'analytique. Où que se trouvent les données ou les utilisateurs, Snowflake offre une expérience de données unique qui s'étend sur plusieurs clouds et géographies. Des milliers de clients dans de nombreuses industries, y compris 691 des 2000 plus grandes entreprises mondiales de Forbes en 2023 (G2K) au 31 janvier, utilisent le AI Data Cloud de Snowflake pour dynamiser leurs entreprises.

**Average Rating:** 4.5/5.0

**Total Reviews:** 713

#### How Do G2 Users Rate Snowflake?

- **the product a-t-il été un bon partenaire commercial?:** 9.0/10 (Category avg: 8.9/10)
- **Analyse multi-sources:** 9.1/10 (Category avg: 8.5/10)
- **Analyse en temps réel:** 9.2/10 (Category avg: 8.5/10)
- **Flux de travail de données:** 9.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

- **Vendeur:** [Snowflake, Inc.](https://www.g2.com/fr/sellers/snowflake-inc)
- **Site Web de l'entreprise:** www.snowflake.com
- **Année de fondation:** 2012
- **Emplacement du siège social:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 abonnés Twitter
- **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ad18ff73a9b8bb34dd1b98a6ba1c6be57f7364939ad352612ecc483aba05d2b2&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsnowflake-computing%2F&secure%5Burl_type%5D=linkedin_company_website)  
11,308 employés sur LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingénieur de données, Analyste de données
- **Top Industries:** Technologie de l'information et services, Logiciels informatiques
- **Company Size:** 45% Medium, 43% Large

#### What Do G2 Reviewers Say About Snowflake?

_AI-generated summary from verified user reviews_

##### Pros

- Les utilisateurs apprécient la **facilité d'utilisation** de Snowflake, le trouvant rapide et efficace pour le partage de données et l'analyse.
- Les utilisateurs apprécient les **fonctionnalités fiables** de Snowflake, profitant de son interface intuitive et de son intégration de données transparente pour l'analyse.
- Les utilisateurs trouvent que les **capacités de gestion des données** de Snowflake sont excellentes pour agréger et interroger efficacement plusieurs ensembles de données.
- Les utilisateurs admirent la **scalabilité transparente** de Snowflake, qui s'adapte sans effort aux exigences de travail et assure des performances optimales.
- Les utilisateurs apprécient la **rapidité de l'analyse des données** de Snowflake, permettant des insights rapides sans soucis d'infrastructure.

##### Cons

- Les utilisateurs trouvent que les **coûts élevés** de Snowflake sont lourds, surtout pour les petites entreprises avec des budgets limités.
- Les utilisateurs trouvent des **limitations de fonctionnalités** dans Snowflake, telles que l'absence de blocs de code et des difficultés dans la gestion des autorisations.
- Les utilisateurs constatent que la **gestion des coûts** nécessite de la discipline, car des frais inattendus peuvent s'accumuler rapidement sans surveillance attentive.
- Les utilisateurs trouvent la **structure des coûts difficile à optimiser** , ce qui entraîne des dépenses initiales plus élevées que prévu lors de la mise en œuvre.
- Les utilisateurs trouvent que les **fonctionnalités limitées** de Snowflake dans les scripts dynamiques et la surveillance entravent la flexibilité et l'utilisabilité.

#### What Are Recent G2 Reviews of Snowflake?

**["Mise à l'échelle élastique et analyses rapides avec Snowflake"](https://www.g2.com/fr/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/fr/survey_responses/snowflake-review-13129003)

**["Snowflake simplifie la gestion des données à grande échelle"](https://www.g2.com/fr/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

[Read full review](https://www.g2.com/fr/survey_responses/snowflake-review-12898129)

#### What Are G2 Users Discussing About Snowflake?

- [What is Snowflake used for?](https://www.g2.com/fr/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote

### [IBM watsonx.data](https://www.g2.com/fr/products/ibm-watsonx-data/reviews)

IBM® watsonx.data® vous aide à accéder, intégrer et comprendre toutes vos données — structurées et non structurées — dans n'importe quel environnement. Il optimise les charges de travail pour le prix et la performance tout en appliquant une gouvernance cohérente à travers les sources, les formats et les équipes. Regardez la démonstration pour apprendre comment watsonx.data vous permet de créer des applications d'IA générative et des agents d'IA puissants. Essai gratuit disponible : https://ibm.biz/Watsonx-data\_Trial

**Average Rating:** 4.4/5.0

**Total Reviews:** 167

#### How Do G2 Users Rate IBM watsonx.data?

- **the product a-t-il été un bon partenaire commercial?:** 8.7/10 (Category avg: 8.9/10)
- **Analyse multi-sources:** 8.5/10 (Category avg: 8.5/10)
- **Analyse en temps réel:** 7.3/10 (Category avg: 8.5/10)
- **Flux de travail de données:** 8.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind IBM watsonx.data?

- **Vendeur:** [IBM](https://www.g2.com/fr/sellers/ibm)
- **Site Web de l'entreprise:** www.ibm.com
- **Année de fondation:** 1911
- **Emplacement du siège social:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 abonnés Twitter
- **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 employés sur LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingénieur logiciel, PDG
- **Top Industries:** Technologie de l'information et services, Logiciels informatiques
- **Company Size:** 34% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.data?

_AI-generated summary from verified user reviews_

##### Pros

- Les utilisateurs apprécient la **facilité d'utilisation** d'IBM watsonx.data, le trouvant fiable et efficace pour les tâches de gestion des données.
- Les utilisateurs apprécient l' **intégration de données organisée** et l'interface intuitive d'IBM watsonx.data, améliorant l'efficacité et l'analyse.
- Les utilisateurs apprécient la **gestion de données organisée et efficace** d'IBM watsonx.data, améliorant les tâches d'analyse et de reporting de manière transparente.
- Les utilisateurs apprécient l' **intégration transparente des sources de données** dans IBM watsonx.data, améliorant la flexibilité et l'efficacité pour divers projets.
- Les utilisateurs apprécient la **capacité à unifier les données à travers des environnements hybrides** , ce qui améliore la flexibilité et favorise une prise de décision éclairée.

##### Cons

- Les utilisateurs trouvent que la **, rendant la configuration initiale et la navigation difficiles pour les nouveaux venus sur IBM watsonx.data.**
- Les utilisateurs trouvent que la **complexité** de la configuration d'IBM watsonx.data est un obstacle, surtout pour les nouveaux venus aux technologies IBM.
- Les utilisateurs trouvent que le **prix est élevé** pour IBM watsonx.data, ce qui le rend moins accessible pour les petites entreprises et les projets.
- Les utilisateurs trouvent que la **configuration difficile** d'IBM watsonx.data est chronophage, avec une courbe d'apprentissage abrupte et des configurations complexes.
- Les utilisateurs trouvent IBM watsonx.data **difficile à naviguer** , surtout pour les débutants et ceux qui ne sont pas familiers avec l'IA et l'analyse de données.

#### What Are Recent G2 Reviews of IBM watsonx.data?

**["Performances de requêtes puissantes et gouvernance, mais une courbe d'apprentissage abrupte pour l'intégration"](https://www.g2.com/fr/survey_responses/ibm-watsonx-data-review-12836202)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/fr/survey_responses/ibm-watsonx-data-review-12836202)

**["Interface utilisateur propre et fluide avec d'excellents visuels d'intégration et d'infrastructure"](https://www.g2.com/fr/survey_responses/ibm-watsonx-data-review-13204444)**

**Rating:** 4.0/5.0 stars

_— Aliasgar B._

[Read full review](https://www.g2.com/fr/survey_responses/ibm-watsonx-data-review-13204444)

### [Azure Databricks](https://www.g2.com/de/products/azure-databricks/reviews)

Azure Databricks ist eine einheitliche, offene Analyseplattform, die gemeinsam von Microsoft und Databricks entwickelt wurde. Basierend auf der Lakehouse-Architektur integriert sie nahtlos Datenengineering, Data Science und maschinelles Lernen innerhalb des Azure-Ökosystems. Diese Plattform vereinfacht die Entwicklung und Bereitstellung datengetriebener Anwendungen, indem sie einen kollaborativen Arbeitsbereich bietet, der mehrere Programmiersprachen unterstützt, darunter SQL, Python, R und Scala. Durch die Nutzung von Azure Databricks können Organisationen große Datenmengen effizient verarbeiten, fortgeschrittene Analysen durchführen und KI-Lösungen entwickeln, während sie von der Skalierbarkeit und Sicherheit von Azure profitieren. Hauptmerkmale und Funktionalität: - Lakehouse-Architektur: Kombiniert die besten Elemente von Data Lakes und Data Warehouses und ermöglicht eine einheitliche Datenspeicherung und Analyse. - Kollaborative Notebooks: Interaktive Arbeitsbereiche, die mehrere Sprachen unterstützen und die Zusammenarbeit zwischen Dateningenieuren, Data Scientists und Analysten erleichtern. - Optimierte Apache Spark Engine: Verbessert die Leistung bei Big-Data-Verarbeitung, um schnellere und zuverlässigere Analysen zu gewährleisten. - Delta Lake Integration: Bietet ACID-Transaktionen und skalierbare Metadatenverwaltung, um die Datenzuverlässigkeit und Konsistenz zu verbessern. - Nahtlose Azure-Integration: Bietet native Konnektivität zu Azure-Diensten wie Power BI, Azure Data Lake Storage und Azure Synapse Analytics, um Daten-Workflows zu optimieren. - Unterstützung für fortgeschrittenes maschinelles Lernen: Beinhaltet vorkonfigurierte Umgebungen für die Entwicklung von maschinellem Lernen und KI, mit Unterstützung für beliebte Frameworks und Bibliotheken. Primärer Wert und bereitgestellte Lösungen: Azure Databricks adressiert die Herausforderungen bei der Verwaltung und Analyse großer Datenmengen, indem es eine skalierbare und kollaborative Plattform bietet, die Datenengineering, Data Science und maschinelles Lernen vereint. Es vereinfacht komplexe Daten-Workflows, beschleunigt die Zeit bis zur Erkenntnis und ermöglicht die Entwicklung von KI-gesteuerten Lösungen. Durch die nahtlose Integration mit Azure-Diensten gewährleistet es eine sichere und effiziente Datenverarbeitung, die Organisationen dabei hilft, datengetriebene Entscheidungen zu treffen und schnell zu innovieren.

**Average Rating:** 4.5/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Azure Databricks?

- **War the product ein guter Geschäftspartner?:** 8.8/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 9.0/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.9/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Databricks?

- **Verkäufer:** [Microsoft](https://www.g2.com/de/sellers/microsoft)
- **Gründungsjahr:** 1975
- **Hauptsitz:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Dateningenieur, Software-Ingenieur
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 47% Large, 28% Medium

#### What Do G2 Reviewers Say About Azure Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer finden Azure Databricks **sehr einfach zu verwenden und zu implementieren** , was ihre Entwicklungserfahrung erheblich verbessert.
- Benutzer schätzen das **umfangreiche Funktionsset** von Azure Databricks und heben seine hervorragende Integration und Unterstützung für mehrere Sprachen hervor.
- Benutzer schätzen die **nahtlosen Integrationen** von Azure Databricks mit Azure-Diensten, die die Effizienz steigern und Arbeitsabläufe vereinfachen.
- Benutzer profitieren von der **beeindruckenden Geschwindigkeit** von Azure Databricks für die Verarbeitung und Implementierung von großen Datenmengen.
- Benutzer schätzen die **Effizienz der Analysen** in Azure Databricks, die eine optimierte Datenverarbeitung und Erkenntnisgewinnung ermöglichen.

##### Cons

- Benutzer finden die **Komplexität** von Azure Databricks herausfordernd, insbesondere während der anfänglichen Einrichtung und des Cluster-Managements.
- Benutzer finden die **schwierige Einrichtung** von Azure Databricks herausfordernd, was die anfängliche Konfiguration zu einem komplexen Prozess macht.
- Benutzer erleben eine **steile Lernkurve** mit Azure Databricks und finden es herausfordernd, sich an dessen Komplexität anzupassen.
- Benutzer erleben **langsame Leistung** mit Azure Databricks, insbesondere während des Cluster-Starts und bei parallelen Verarbeitungsvorgängen.
- Benutzer finden oft **unklare Preisgestaltung** für Azure Databricks, da die Kosten ohne ordnungsgemäße Überwachung schnell steigen können.

#### What Are Recent G2 Reviews of Azure Databricks?

**["Ausbalancieren von Leistung und Komplexität in Azure Databricks"](https://www.g2.com/de/survey_responses/azure-databricks-review-12601026)**

**Rating:** 5.0/5.0 stars

_— Ravi V._

[Read full review](https://www.g2.com/de/survey_responses/azure-databricks-review-12601026)

**["Azure Databricks effizient für große Datenmengen, etwas rau an den Rändern"](https://www.g2.com/de/survey_responses/azure-databricks-review-12684122)**

**Rating:** 4.5/5.0 stars

_— Wealth A._

[Read full review](https://www.g2.com/de/survey_responses/azure-databricks-review-12684122)

#### What Are G2 Users Discussing About Azure Databricks?

- [Wofür wird Azure Databricks verwendet?](https://www.g2.com/de/discussions/azure-databricks-what-is-azure-databricks-used-for) - 2 comments
- [Is Azure Databricks PaaS or SAAS?](https://www.g2.com/de/discussions/is-azure-databricks-paas-or-saas) - 2 comments
- [Besitzt Microsoft Databricks?](https://www.g2.com/de/discussions/does-microsoft-own-databricks) - 2 comments
- [Was ist Databricks Azure?](https://www.g2.com/de/discussions/what-is-databricks-azure) - 1 comment
- [What is azure Databricks used for?](https://www.g2.com/de/discussions/what-is-azure-databricks-used-for)

### [Alteryx](https://www.g2.com/de/products/alteryx/reviews)

Alteryx hilft Unternehmen über seine Alteryx One-Plattform, komplexe, unverbundene Daten in einen sauberen, KI-bereiten Zustand zu transformieren. Egal, ob Sie Finanzprognosen erstellen, die Leistung von Lieferanten analysieren, Kundendaten segmentieren, die Mitarbeiterbindung analysieren oder wettbewerbsfähige KI-Anwendungen aus Ihren proprietären Daten entwickeln, Alteryx One macht es einfach, Daten zu bereinigen, zu mischen und zu analysieren, um die einzigartigen Erkenntnisse freizuschalten, die zu wirkungsvollen Entscheidungen führen. KI-gestützte Analysen Alteryx automatisiert und vereinfacht jede Phase der Datenvorbereitung und -analyse, von der Validierung und Anreicherung bis hin zu prädiktiven Analysen und automatisierten Erkenntnissen. Integrieren Sie generative KI direkt in Ihre Workflows, um komplexe Datenaufgaben zu rationalisieren und schneller Erkenntnisse zu gewinnen. Unübertroffene Flexibilität, egal ob Sie codefreie Workflows, natürliche Sprachbefehle oder Low-Code-Optionen bevorzugen, Alteryx passt sich Ihren Bedürfnissen an. Vertrauenswürdig. Sicher. Unternehmensbereit. Alteryx wird von über der Hälfte der Global 2000 und 19 der 20 größten globalen Banken vertraut. Mit integrierter Automatisierung, Governance und Sicherheit können Ihre Workflows skalieren und die Compliance aufrechterhalten, während sie konsistente Ergebnisse liefern. Und es spielt keine Rolle, ob Ihre Systeme vor Ort, hybrid oder in der Cloud sind; Alteryx passt sich mühelos in Ihre Infrastruktur ein. Einfach zu bedienen. Tief verbunden. Was Alteryx wirklich auszeichnet, ist unser Fokus auf Effizienz und Benutzerfreundlichkeit für Analysten und unsere aktive Community von 700.000 Alteryx-Nutzern, die Sie bei jedem Schritt Ihrer Reise unterstützen. Mit nahtloser Integration in Daten überall, einschließlich Plattformen wie Databricks, Snowflake, AWS, Google, SAP und Salesforce, hilft unsere Plattform, isolierte Daten zu vereinheitlichen und die Gewinnung von Erkenntnissen zu beschleunigen. Besuchen Sie Alteryx.com für weitere Informationen und um Ihre kostenlose Testversion zu starten.

**Average Rating:** 4.6/5.0

**Total Reviews:** 859

#### How Do G2 Users Rate Alteryx?

- **War the product ein guter Geschäftspartner?:** 8.8/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 9.0/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.4/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 9.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Alteryx?

- **Verkäufer:** [Alteryx](https://www.g2.com/de/sellers/alteryx)
- **Unternehmenswebsite:** www.alteryx.com
- **Gründungsjahr:** 1997
- **Hauptsitz:** Irvine, CA
- **Twitter:** @alteryx  
26,149 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ae8a7629c5a6d593caff29361a6ee3fb670df11992dd94a9656c66461078b340&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F903031%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,304 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Datenanalyst, Analyst
- **Top Industries:** Finanzdienstleistungen, Buchhaltung
- **Company Size:** 63% Large, 21% Medium

#### What Do G2 Reviewers Say About Alteryx?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Benutzerfreundlichkeit** von Alteryx und finden es benutzerfreundlich und effizient für nicht-technische Benutzer.
- Benutzer schätzen die **Automatisierungsfähigkeiten** von Alteryx, die Geschwindigkeit und Effizienz bei der Datenvorbereitung und -analyse verbessern.
- Benutzer lieben das **intuitive Design** von Alteryx, das Datenmanagement und die Erstellung von Workflows mühelos und effizient macht.
- Benutzer finden Alteryx **sehr einfach zu erlernen und zu verwenden** , was ihre Daten-Workflow- und Automatisierungserfahrung verbessert.
- Benutzer schätzen die **Effizienz** von Alteryx, die eine schnelle Datenverarbeitung und optimierte Arbeitsabläufe ohne komplexe Programmierung ermöglicht.

##### Cons

- Benutzer erwähnen, dass Alteryx **hohe Kosten** hat, was für kleine Teams und Startups eine Herausforderung sein kann.
- Benutzer finden eine **steile Lernkurve** für fortgeschrittene Funktionen, was es für Anfänger schwierig macht, Alteryx schnell zu meistern.
- Benutzer weisen auf die **fehlenden Funktionen** in Alteryx hin, wie begrenzte Konnektoren und Probleme mit der Ausgabe-Flexibilität.
- Benutzer finden **Lernschwierigkeiten** in Alteryx aufgrund verwirrender Werkzeuge und Fehlerbehebung, insbesondere für Anfänger.
- Benutzer stoßen auf **langsame Leistung** bei der Verarbeitung großer Datensätze, was die Effizienz und Benutzerfreundlichkeit in Alteryx beeinträchtigt.

#### What Are Recent G2 Reviews of Alteryx?

**["Skaliert Operationen und spart Zeit mit automatisierten Daten-Workflows"](https://www.g2.com/de/survey_responses/alteryx-review-13047637)**

**Rating:** 4.5/5.0 stars

_— Ihor B._

[Read full review](https://www.g2.com/de/survey_responses/alteryx-review-13047637)

**["Macht die Datenvorbereitung schneller mit einem intuitiven Drag-and-Drop-Workflow"](https://www.g2.com/de/survey_responses/alteryx-review-13190742)**

**Rating:** 4.5/5.0 stars

_— Anushka S._

[Read full review](https://www.g2.com/de/survey_responses/alteryx-review-13190742)

### [Kyvos Semantic Layer](https://www.g2.com/fr/products/kyvos-semantic-layer/reviews)

Kyvos est une couche sémantique pour l'IA et la BI. Il offre aux organisations une vue unique, cohérente et conviviale de l'ensemble de leur patrimoine de données. En standardisant la manière dont les données sont définies et comprises, Kyvos élimine la dérive des métriques à travers les outils de BI et garantit que les LLM et les agents d'IA travaillent avec des sémantiques commerciales gouvernées plutôt qu'avec des tables brutes. Kyvos offre également des analyses ultra-rapides à grande échelle et à haute concurrence — y compris une analyse multidimensionnelle granulaire sur le cloud — sans les temps de requête lents et les coûts croissants du cloud qui les accompagnent généralement. Pourquoi les organisations utilisent Kyvos Fondation Sémantique Unifiée pour l'IA et la BI La couche sémantique de Kyvos standardise la manière dont les métriques, les KPI, les dimensions, les hiérarchies, les relations, les calculs et les règles commerciales sont modélisés à travers l'entreprise — afin que les tableaux de bord, les outils d'analyse, les notebooks et les systèmes d'IA fonctionnent tous sur la même compréhension de l'entreprise. Kyvos permet : - Sémantique partagée — un langage de données commun à chaque outil, équipe et système - Accès gouverné — exploration des données dans des limites de sécurité, de rôle et de permission définies - Interopérabilité de la plateforme — contexte sémantique cohérent à travers des plateformes et environnements divers - Préparation à l'IA — les LLM et les agents travaillent avec des sémantiques commerciales gouvernées plutôt qu'avec des tables brutes ou des schémas ambigus IA Ancrée dans le Contexte Commercial Kyvos ancre les systèmes d'IA dans le modèle sémantique gouverné, garantissant qu'ils fonctionnent sur un contexte commercial établi plutôt que sur des schémas bruts — améliorant la précision, la traçabilité et la fiabilité des insights générés par l'IA. Métriques Cohérentes à Travers les Outils de BI Kyvos centralise les définitions des métriques et des KPI dans la couche sémantique et les applique de manière cohérente à travers chaque interface d'analyse — éliminant la dérive des métriques et améliorant la confiance dans les analyses. Analytique Haute Performance à Grande Échelle Kyvos offre des analyses haute performance qui s'adaptent à la demande, permettant : - Performance de requête en sous-seconde à travers des ensembles de données massifs - Haute concurrence à travers des milliers d'utilisateurs et de charges de travail - Temps de réponse cohérents indépendamment du volume de données ou de la concurrence - Aucune dégradation des performances à mesure que l'adoption augmente - Analytique Multidimensionnelle sur le Cloud Kyvos permet une analyse multidimensionnelle approfondie, soutenant : - Analyse granulaire à travers des milliards de lignes - Des milliers de mesures et de dimensions dans un seul modèle - Exploration rapide à travers des hiérarchies complexes - Profondeur analytique complète sans sacrifier la vitesse de requête Efficacité des Coûts du Cloud Kyvos sert des analyses à travers sa couche sémantique plutôt que de router chaque requête vers l'entrepôt — réduisant la consommation de calcul à travers les charges de travail d'analyse et d'IA. À mesure que l'adoption augmente, les organisations peuvent faire évoluer les utilisateurs, les charges de travail et la complexité analytique sans une augmentation correspondante des coûts de calcul de l'entrepôt.

**Average Rating:** 4.8/5.0

**Total Reviews:** 267

#### How Do G2 Users Rate Kyvos Semantic Layer?

- **the product a-t-il été un bon partenaire commercial?:** 9.6/10 (Category avg: 8.9/10)
- **Analyse multi-sources:** 9.2/10 (Category avg: 8.5/10)
- **Analyse en temps réel:** 10.0/10 (Category avg: 8.5/10)
- **Flux de travail de données:** 9.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Kyvos Semantic Layer?

- **Vendeur:** [Kyvos Insights](https://www.g2.com/fr/sellers/kyvos-insights)
- **Site Web de l'entreprise:** www.kyvosinsights.com
- **Année de fondation:** 2014
- **Emplacement du siège social:** Los Gatos, CA
- **Twitter:** @KyvosInsights  
689 abonnés Twitter
- **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=900350c47a6a807c4765a28f52dcdbf3c06a5325a45d905c54e56a79e545cf9d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkyvos-insights-inc-%2F&secure%5Burl_type%5D=linkedin_company_website)  
152 employés sur LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingénieur Logiciel Senior, Ingénieur logiciel
- **Top Industries:** Technologie de l'information et services, Logiciels informatiques
- **Company Size:** 57% Medium, 38% Large

#### What Do G2 Reviewers Say About Kyvos Semantic Layer?

_AI-generated summary from verified user reviews_

##### Pros

- Les utilisateurs apprécient la **facilité d'utilisation** de Kyvos, permettant des insights rapides et une expérience conviviale pour des données complexes.
- Les utilisateurs adorent la **vitesse** de Kyvos pour des insights en temps réel, permettant des requêtes rapides et une prise de décision plus rapide à travers les métriques de données.
- Les utilisateurs apprécient la **performance exceptionnelle** de Kyvos pour analyser rapidement de grands ensembles de données et fournir des informations en temps opportun.
- Les utilisateurs admirent les **analyses ultra-rapides** de la couche sémantique Kyvos, améliorant la performance et la visualisation de grands ensembles de données.
- Les utilisateurs apprécient les **capacités de requête rapide** de la couche sémantique Kyvos, permettant une analyse rapide de grands ensembles de données transactionnelles.

##### Cons

- Les utilisateurs trouvent que la **pour les fonctionnalités avancées et les requêtes MDX, ce qui peut ralentir les efforts d'utilisation.**
- Les utilisateurs trouvent que la **configuration difficile** de la couche sémantique Kyvos est un défi, bien que le support aide à faciliter le processus.
- Les utilisateurs trouvent la **configuration initiale et la complexité de MDX** difficiles, bien que le support aide à faciliter le processus de déploiement.
- Les utilisateurs notent les **limitations des fonctionnalités** de Kyvos, notamment le manque d'analyses avancées et d'intégration pour une exploration de données fluide.
- Les utilisateurs rencontrent des **problèmes de connectivité** , car l'intégration initiale avec les systèmes existants peut prendre du temps.

#### What Are Recent G2 Reviews of Kyvos Semantic Layer?

**["Exploration rapide et cohérente des données à travers les dimensions avec la couche sémantique Kyvos"](https://www.g2.com/fr/survey_responses/kyvos-semantic-layer-review-12911098)**

**Rating:** 5.0/5.0 stars

_— ashish r._

[Read full review](https://www.g2.com/fr/survey_responses/kyvos-semantic-layer-review-12911098)

**["La couche sémantique de Kyvos améliore la précision de l'IA avec des données prêtes pour les affaires."](https://www.g2.com/fr/survey_responses/kyvos-semantic-layer-review-13142366)**

**Rating:** 5.0/5.0 stars

_— Nikhil K._

[Read full review](https://www.g2.com/fr/survey_responses/kyvos-semantic-layer-review-13142366)

### [Azure Synapse Analytics](https://www.g2.com/de/products/azure-synapse-analytics/reviews)

Azure Synapse Analytics ist ein cloudbasiertes Enterprise Data Warehouse (EDW), das Massively Parallel Processing (MPP) nutzt, um komplexe Abfragen über Petabytes von Daten schnell auszuführen.

**Average Rating:** 4.4/5.0

**Total Reviews:** 37

#### How Do G2 Users Rate Azure Synapse Analytics?

- **War the product ein guter Geschäftspartner?:** 8.3/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 8.9/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.9/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 8.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Synapse Analytics?

- **Verkäufer:** [Microsoft](https://www.g2.com/de/sellers/microsoft)
- **Gründungsjahr:** 1975
- **Hauptsitz:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** MSFT

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen
- **Company Size:** 45% Medium, 32% Large

#### What Do G2 Reviewers Say About Azure Synapse Analytics?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer loben das **einheitliche Analyseerlebnis** von Azure Synapse Analytics, das die Effizienz steigert und komplexe Datenprozesse vereinfacht.
- Benutzer schätzen die **Automatisierungsfähigkeiten** von Azure Synapse Analytics, die die Effizienz in Datenanalyselösungen verbessern.
- Benutzer schätzen die **nahtlose Cloud-Integration** von Azure Synapse Analytics, die Datenworkflows und die Gesamteffizienz verbessert.
- Benutzer schätzen die **kosteneffizienten** Fähigkeiten von Azure Synapse Analytics und genießen skalierbare Lösungen ohne hohe Ausgaben.
- Benutzer schätzen die **nahtlose Datenintegration** von Azure Synapse Analytics, die Effizienz steigert und Analyselösungen vereinfacht.

##### Cons

- Benutzer finden den **Kostenschätzungsprozess komplex** aufgrund von Schwierigkeiten bei der Überwachung und Optimierung verschiedener Dienstkomponenten.
- Benutzer stehen vor Herausforderungen bei der **Kostenverwaltung** und kämpfen mit der Optimierung und Überwachung über verschiedene Azure Synapse-Komponenten hinweg.
- Benutzer stehen vor Herausforderungen bei der **Fehlerbehebung komplexer Pipeline-Ausfälle** aufgrund eines Mangels an detaillierter Fehlertransparenz, was die Fehlersuche verlängert.
- Benutzer stehen vor **schwierigen Debugging** -Aufgaben aufgrund einer steilen Lernkurve und mangelnder detaillierter Fehlertransparenz bei Pipeline-Ausfällen.
- Benutzer finden Azure Synapse Analytics **teuer** , insbesondere bei der Verwaltung von Kosten über mehrere Dienste und Abfragen hinweg.

#### What Are Recent G2 Reviews of Azure Synapse Analytics?

**["Vereinheitlichte Datenlagerung und Big Data in einer leistungsstarken Plattform"](https://www.g2.com/de/survey_responses/azure-synapse-analytics-review-12435130)**

**Rating:** 4.5/5.0 stars

_— Daniel H._

[Read full review](https://www.g2.com/de/survey_responses/azure-synapse-analytics-review-12435130)

**["Einheitliche Analytik-Plattform mit nahtloser Azure-Integration"](https://www.g2.com/de/survey_responses/azure-synapse-analytics-review-12353239)**

**Rating:** 4.0/5.0 stars

_— Ashish D._

[Read full review](https://www.g2.com/de/survey_responses/azure-synapse-analytics-review-12353239)

#### What Are G2 Users Discussing About Azure Synapse Analytics?

- [Does Azure Synapse include Analysis Services?](https://www.g2.com/de/discussions/does-azure-synapse-include-analysis-services)
- [When should use Azure synapse analytics?](https://www.g2.com/de/discussions/when-should-use-azure-synapse-analytics)
- [What are advantages of Azure synapse analytics?](https://www.g2.com/de/discussions/what-are-advantages-of-azure-synapse-analytics)
- [What is included in Azure synapse analytics?](https://www.g2.com/de/discussions/what-is-included-in-azure-synapse-analytics)

### [Dataiku](https://www.g2.com/fr/products/dataiku/reviews)

Dataiku est la plateforme pour le succès de l'IA : la couche d'orchestration de l'IA où les entreprises construisent, déploient et gouvernent des analyses, des modèles et des agents à grande échelle. Elle se situe au-dessus des plateformes de données, des clouds et des services d'IA que vous utilisez déjà, fonctionnant à travers tous sans vous enfermer dans un seul. Dataiku élargit le cercle de ceux qui peuvent construire de l'IA en production, mettant les bons outils entre les mains des data scientists et des experts du domaine, qu'il s'agisse d'analystes de fraude ou de planificateurs de la demande. Elle orchestre l'apprentissage automatique, les règles, les LLMs et les agents comme un système gouverné unique, construit sur plus d'une décennie de gestion de l'IA en production. La gouvernance fait partie de la construction plutôt que d'être ajoutée après coup, permettant ainsi aux équipes de livrer plus rapidement tout en gardant la performance, le coût et le risque sous contrôle. Le résultat : une IA qui passe de l'expérimentation à une exécution fiable et mesurable maintenant, et non dans 18 mois.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **the product a-t-il été un bon partenaire commercial?:** 8.6/10 (Category avg: 8.9/10)
- **Analyse multi-sources:** 8.8/10 (Category avg: 8.5/10)
- **Analyse en temps réel:** 8.6/10 (Category avg: 8.5/10)
- **Flux de travail de données:** 9.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Dataiku?

- **Vendeur:** [Dataiku](https://www.g2.com/fr/sellers/dataiku)
- **Site Web de l'entreprise:** Dataiku.com
- **Année de fondation:** 2013
- **Emplacement du siège social:** New York, NY
- **Twitter:** @dataiku  
22,917 abonnés Twitter
- **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e59ec8fccc02ecc4f883419e54da56d3f6fc8b1e556153f0cc01cd05e3b77faa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataiku%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,619 employés sur LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Scientifique des données, Analyste de données
- **Top Industries:** Services financiers, Pharmaceutique
- **Company Size:** 60% Large, 22% Medium

#### What Do G2 Reviewers Say About Dataiku?

_AI-generated summary from verified user reviews_

##### Pros

- Les utilisateurs apprécient comment Dataiku facilite le **développement ML facile** , permettant de se concentrer sur la construction de modèles sans la complexité.
- Les utilisateurs adorent la **facilité d'utilisation** de Dataiku, qui simplifie les tâches complexes et améliore leur expérience d'analyse de données.
- Les utilisateurs apprécient la **facilité d'utilisation** de Dataiku, permettant la collaboration entre utilisateurs techniques et non techniques.
- Les utilisateurs apprécient les **intégrations faciles** de Dataiku, facilitant une collaboration fluide et un déploiement à travers divers outils d'analyse.
- Les utilisateurs bénéficient de l' **amélioration de la productivité** de Dataiku, permettant un développement de projet plus rapide et une croissance professionnelle accrue.

##### Cons

- Les utilisateurs trouvent la **courbe d'apprentissage abrupte** de Dataiku difficile, rendant la maîtrise de la plateforme ardue pour les débutants.
- Les utilisateurs trouvent la **courbe d'apprentissage abrupte** difficile pour les débutants, ce qui affecte leur capacité à utiliser efficacement Dataiku.
- Les utilisateurs trouvent la **courbe d'apprentissage difficile** difficile, en particulier pour les débutants naviguant dans les fonctionnalités avancées.
- Les utilisateurs rencontrent une **performance lente** avec Dataiku lorsqu'ils traitent de grands ensembles de données, ce qui affecte l'efficacité et la productivité.
- Les utilisateurs trouvent Dataiku **cher** , surtout pour les petites organisations et projets, ce qui impacte l'accessibilité et l'abordabilité.

#### What Are Recent G2 Reviews of Dataiku?

**["Plateforme unifiée et low-code qui stimule la productivité des données et de l'IA de bout en bout"](https://www.g2.com/fr/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/fr/survey_responses/dataiku-review-13125252)

**["Construisez des flux de travail plus rapides avec des données connectées provenant de nombreux fournisseurs ou de sources de données distinctes."](https://www.g2.com/fr/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/fr/survey_responses/dataiku-review-13120436)

#### What Are G2 Users Discussing About Dataiku?

- [Is Dataiku an ETL tool?](https://www.g2.com/fr/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/fr/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/fr/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/fr/discussions/what-is-dataiku-dss-used-for)

### [Splunk Enterprise](https://www.g2.com/de/products/splunk-enterprise/reviews)

Erfahren Sie, was in Ihrem Unternehmen passiert, und ergreifen Sie schnell sinnvolle Maßnahmen mit Splunk Enterprise. Automatisieren Sie die Sammlung, Indizierung und Benachrichtigung von Maschinendaten, die für Ihre Abläufe entscheidend sind. Entdecken Sie die umsetzbaren Erkenntnisse aus all Ihren Daten – unabhängig von Quelle oder Format. Nutzen Sie künstliche Intelligenz und maschinelles Lernen für vorausschauende und proaktive Geschäftsentscheidungen.

**Average Rating:** 4.3/5.0

**Total Reviews:** 415

#### How Do G2 Users Rate Splunk Enterprise?

- **War the product ein guter Geschäftspartner?:** 8.7/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 8.4/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.7/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 9.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Splunk Enterprise?

- **Verkäufer:** [Cisco](https://www.g2.com/de/sellers/cisco)
- **Gründungsjahr:** 1984
- **Hauptsitz:** San Jose, CA
- **Twitter:** @Cisco  
720,366 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=476aeabc5a712d049453edd5c54ea0318890d9e60d93782e37fe028224df1cbd&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcisco%2F&secure%5Burl_type%5D=linkedin_company_website)  
95,545 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** NASDAQ:CSCO

#### Who Uses This Product?

- **Who Uses This:** Software-Ingenieur, Senior Software Engineer
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 64% Large, 27% Medium

#### What Do G2 Reviewers Say About Splunk Enterprise?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Innovation** von Splunk Enterprise und schätzen seine benutzerfreundlichen Funktionen und leistungsstarken Analysefähigkeiten.
- Benutzer schätzen die **Anpassungsfunktionen** von Splunk Enterprise, die maßgeschneiderte Einblicke und dynamische Dashboards für effektives Monitoring ermöglichen.
- Benutzer loben die **Benutzerfreundlichkeit** von Splunk Enterprise, was die effektive Überwachung und schnelle Problemlösung verbessert.
- Benutzer schätzen die **effizienten Protokollverwaltungs** fähigkeiten von Splunk Enterprise für genaue Analysen und Einblicke.
- Benutzer schätzen die **leistungsstarken Berichtsfunktionen** von Splunk Enterprise, die die Datenanalyse- und Visualisierungsfähigkeiten erheblich verbessern.

##### Cons

- Benutzer finden Splunk Enterprise **teuer** , insbesondere wenn die Datenmengen wachsen, was die Abläufe kleinerer Teams beeinträchtigt.
- Benutzer stehen bei Splunk Enterprise vor einer **steilen Lernkurve** , was das schnelle Beherrschen seiner Funktionen erschweren kann.
- Benutzer heben die **teuren Lizenzgebühren** von Splunk Enterprise hervor, was es für einige Unternehmen schwierig macht, es zu übernehmen.
- Benutzer stehen vor **Integrationsproblemen** mit Splunk Enterprise, was bessere Add-ons und eine einfachere Architektur für eine leichtere Bereitstellung erfordert.
- Benutzer empfinden, dass Splunk Enterprise **fehlende Funktionen** hat, wichtige Add-ons fehlen und flexiblere Optionen zur Datenaufnahme benötigt werden.

#### What Are Recent G2 Reviews of Splunk Enterprise?

**["Ausgezeichnete Lösung für Unternehmensüberwachung und Log-Management für hybride Cloud-Infrastruktur"](https://www.g2.com/de/survey_responses/splunk-enterprise-review-12045230)**

**Rating:** 4.5/5.0 stars

_— RaviShankar S._

[Read full review](https://www.g2.com/de/survey_responses/splunk-enterprise-review-12045230)

**["SPL-Suche und Dashboards sind wirklich nützlich"](https://www.g2.com/de/survey_responses/splunk-enterprise-review-12547655)**

**Rating:** 4.0/5.0 stars

_— Nishith J._

[Read full review](https://www.g2.com/de/survey_responses/splunk-enterprise-review-12547655)

#### What Are G2 Users Discussing About Splunk Enterprise?

- [What is Splunk Enterprise used for?](https://www.g2.com/de/discussions/what-is-splunk-enterprise-used-for) - 1 comment
- [Was ist der Unterschied zwischen Splunk Enterprise und Splunk Enterprise Security?](https://www.g2.com/de/discussions/splunk-enterprise-what-is-the-difference-between-splunk-enterprise-and-splunk-enterprise-security) - 1 comment
- [Was sind die Komponenten von Splunk Enterprise?](https://www.g2.com/de/discussions/what-are-splunk-enterprise-components) - 1 comment
- [Welche Apps werden mit Splunk Enterprise geliefert?](https://www.g2.com/de/discussions/which-apps-ship-with-splunk-enterprise) - 1 comment
- [Was macht Splunk Enterprise?](https://www.g2.com/de/discussions/what-does-splunk-enterprise-do) - 1 comment

### [dbt](https://www.g2.com/de/products/dbt/reviews)

dbt ist ein Transformations-Workflow, der es Datenteams ermöglicht, Analytik-Code schnell und kollaborativ bereitzustellen, indem er Best Practices der Softwareentwicklung wie Modularität, Portabilität, CI/CD und Dokumentation befolgt. Jetzt kann jeder, der SQL kennt, produktionsreife Datenpipelines erstellen.

**Average Rating:** 4.7/5.0

**Total Reviews:** 208

#### How Do G2 Users Rate dbt?

- **War the product ein guter Geschäftspartner?:** 8.6/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 8.5/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.5/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 9.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind dbt?

- **Verkäufer:** [Fivetran](https://www.g2.com/de/sellers/fivetran)
- **Gründungsjahr:** 2012
- **Hauptsitz:** Oakland, CA
- **Twitter:** @fivetran  
5,767 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7f1ca0a984cd3b7fe678e6cf023235ee7978629644faf3910a5df7f92b63eea3&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ffivetran%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,848 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Dateningenieur, Analytik-Ingenieur
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 56% Medium, 27% Small

#### What Do G2 Reviewers Say About dbt?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer lieben die **Benutzerfreundlichkeit** von dbt, dank seiner klaren Struktur, intuitiven Dokumentation und nahtlosen Integration.
- Benutzer schätzen dbt für seine **Integration von Best Practices der Softwareentwicklung** , die die Wartbarkeit und Zusammenarbeit bei SQL-Transformationen verbessern.
- Benutzer schätzen die **Automatisierungs** funktionen von dbt, die die Wartbarkeit von SQL-Code erheblich verbessern und Daten-Workflows transformieren.
- Benutzer schätzen die **transformative Kraft** von dbt, das Daten effizient organisiert und modelliert, um umsetzbare Erkenntnisse zu gewinnen.
- Benutzer schätzen dbt für seine **hohe Datenqualität** , die Integrität gewährleistet und Analyse-Workflows durch Modularisierung und Dokumentation verbessert.

##### Cons

- Benutzer stehen vor Herausforderungen mit **eingeschränkter Funktionalität** in dbt aufgrund starrer Modelle und Debugging-Schwierigkeiten, die den Projektfortschritt beeinträchtigen.
- Benutzer stehen oft vor **Abhängigkeitsproblemen** mit dbt, was zu zeitaufwändigem Troubleshooting und Unterbrechungen in den Arbeitsabläufen führt.
- Benutzer finden die **steile Lernkurve** beim Beherrschen von Konzepten wie Jinja und Git ziemlich herausfordernd.
- Benutzer kämpfen mit **nicht hilfreichen Fehlermeldungen** in dbt, was die Fehlersuche schwierig und frustrierend macht.
- Benutzer stehen vor **verwirrender Fehlerberichterstattung** , die die Fehlersuche erschwert und die schnelle Identifizierung von Problemen behindert.

#### What Are Recent G2 Reviews of dbt?

**["Einfache SQL-gesteuerte Materialisierungen mit leistungsstarker Abstammung"](https://www.g2.com/de/survey_responses/dbt-review-12985641)**

**Rating:** 5.0/5.0 stars

_— Anish G._

[Read full review](https://www.g2.com/de/survey_responses/dbt-review-12985641)

**["dbt optimiert Datenpipelines mit leistungsstarken inkrementellen und SCD2-Funktionen"](https://www.g2.com/de/survey_responses/dbt-review-12712114)**

**Rating:** 5.0/5.0 stars

_— Hithesh P._

[Read full review](https://www.g2.com/de/survey_responses/dbt-review-12712114)

#### What Are G2 Users Discussing About dbt?

- [Was ist DBT-Datenmodellierung?](https://www.g2.com/de/discussions/what-is-dbt-data-modelling) - 2 comments
- [Was ist DBT-Technologie?](https://www.g2.com/de/discussions/what-is-dbt-technology) - 2 comments
- [What is DBT database tool?](https://www.g2.com/de/discussions/what-is-dbt-database-tool) - 1 comment
- [Wofür wird das DBT-Tool verwendet?](https://www.g2.com/de/discussions/what-is-dbt-tool-used-for) - 2 comments

### [Teradata Autonomous Knowledge Platform](https://www.g2.com/de/products/teradata-autonomous-knowledge-platform/reviews)

Die Teradata Autonomous Knowledge Platform aktiviert Unternehmensintelligenz, indem sie Daten, Wissen und Geschäftskontext vereint, um greifbare Ergebnisse zu erzielen. Mit Teradata können Organisationen Agenten den vollständigen Kontext für Wirkung bereitstellen, wenn es darauf ankommt. Unsere Lösung ermöglicht es Unternehmen, sich vor Ort, in der Cloud oder über einen hybriden Ansatz zu verbinden und zu skalieren. Teradata liefert echten Geschäftswert mit KI. Erfahren Sie mehr auf Teradata.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 356

#### How Do G2 Users Rate Teradata Autonomous Knowledge Platform?

- **War the product ein guter Geschäftspartner?:** 8.2/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 7.9/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.2/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 7.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Teradata Autonomous Knowledge Platform?

- **Verkäufer:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/de/sellers/teradata-autonomous-knowledge-platform)
- **Gründungsjahr:** 1979
- **Hauptsitz:** San Diego, CA
- **Twitter:** @Teradata  
93,113 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=06895b9a8db4fa478ba7da480ccd214a14ef698abd028e4642e62f189e82b650&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1466%2F&secure%5Burl_type%5D=linkedin_company_website)  
9,941 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** NYSE:TDC

#### Who Uses This Product?

- **Who Uses This:** Dateningenieur, Software-Ingenieur
- **Top Industries:** Informationstechnologie und Dienstleistungen, Finanzdienstleistungen
- **Company Size:** 69% Large, 22% Medium

#### What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer heben die **extreme Leistung** der Teradata Autonomous Knowledge Platform hervor, insbesondere bei der effizienten Verarbeitung großer Datenmengen.
- Benutzer schätzen die **hohe Leistung der Abfrageausführung** in Teradata, was ihre Fähigkeiten zur Geschäftsanalyse erheblich verbessert.
- Benutzer schätzen die **Skalierbarkeit** der Teradata Autonomous Knowledge Platform, die die Datenintegration und die betriebliche Effizienz erheblich verbessert.
- Benutzer loben die **hohe Leistung und Geschwindigkeit** von Teradata, das große Datensätze effizient und ohne Probleme verarbeitet.
- Benutzer schätzen die **schnelle Verarbeitung großer Datensätze** mit Teradata und loben seine Leistung und Stabilität während der Operationen.

##### Cons

- Benutzer finden die **steile Lernkurve** der Teradata Autonomous Knowledge Platform herausfordernd, was die Einführung und Produktivität vorübergehend beeinträchtigt.
- Benutzer finden die **steile Lernkurve** der Teradata Autonomous Knowledge Platform herausfordernd, insbesondere für diejenigen, die keine technische Expertise haben.
- Benutzer finden die **Komplexität** der Teradata-Plattform herausfordernd, insbesondere für nicht-technische Benutzer und neue Anwender.
- Benutzer äußern Bedenken über die **Anforderungen an das Kostenmanagement** , die erforderlich sind, um potenziellen Missbrauch und Leistungsprobleme zu vermeiden.
- Benutzer empfinden die **hohen Kosten** der Teradata Autonomous Knowledge Platform als einen erheblichen Nachteil, der die Zugänglichkeit beeinträchtigt.

#### What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform?

**["Teradata Vantage Schnelle Abfrageleistung und Starke Analysen für Big Data"](https://www.g2.com/de/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)**

**Rating:** 5.0/5.0 stars

_— Muzammil M._

[Read full review](https://www.g2.com/de/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)

**["Teradata Vantage glänzt bei der Verarbeitung großer Datenmengen und fortschrittlicher Analysen"](https://www.g2.com/de/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)**

**Rating:** 4.5/5.0 stars

_— Nijat I._

[Read full review](https://www.g2.com/de/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)

#### What Are G2 Users Discussing About Teradata Autonomous Knowledge Platform?

- [What does Teradata Data Lab do?](https://www.g2.com/de/discussions/what-does-teradata-data-lab-do)
- [Is Teradata a premiership?](https://www.g2.com/de/discussions/is-teradata-a-premiership)
- [What is Teradata Vantage?](https://www.g2.com/de/discussions/what-is-teradata-vantage)
- [How much does Teradata cost?](https://www.g2.com/de/discussions/how-much-does-teradata-cost)
- [What is Sandbox in Teradata?](https://www.g2.com/de/discussions/what-is-sandbox-in-teradata)

### [Confluent](https://www.g2.com/de/products/confluent/reviews)

Cloud-nativer Dienst für Daten in Bewegung, entwickelt von den ursprünglichen Schöpfern von Apache Kafka® Die heutigen Verbraucher haben die Welt in ihren Händen und erwarten unerbittlich End-to-End-Echtzeit-Marken-Erlebnisse. Daten in Bewegung sind die zugrunde liegende, grundlegende Zutat für jede wirklich vernetzte Kundenerfahrung. Sie bieten eine kontinuierliche Versorgung mit Echtzeit-Ereignisströmen, gekoppelt mit Echtzeit-Stream-Verarbeitung, um die datengesteuerten Backend-Operationen und reichhaltigen Frontend-Erlebnisse zu ermöglichen, die für den Erfolg eines Unternehmens in den heutigen wettbewerbsintensiven, verbraucherorientierten Märkten notwendig sind. Confluent Cloud, entwickelt von den ursprünglichen Schöpfern von Apache Kafka, ist ein vollständig verwalteter, cloud-nativer Dienst zum Verbinden und Verarbeiten all Ihrer Echtzeit-Daten, überall dort, wo sie benötigt werden.

**Average Rating:** 4.4/5.0

**Total Reviews:** 111

#### How Do G2 Users Rate Confluent?

- **War the product ein guter Geschäftspartner?:** 8.5/10 (Category avg: 8.9/10)
- **Multi-Source-Analyse:** 8.3/10 (Category avg: 8.5/10)
- **Echtzeit-Analysen:** 8.9/10 (Category avg: 8.5/10)
- **Daten-Workflow:** 7.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Confluent?

- **Verkäufer:** [IBM](https://www.g2.com/de/sellers/ibm)
- **Gründungsjahr:** 1911
- **Hauptsitz:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** SWX:IBM

#### Who Uses This Product?

- **Who Uses This:** Software-Ingenieur, Senior Software Engineer
- **Top Industries:** Computersoftware, Informationstechnologie und Dienstleistungen
- **Company Size:** 36% Large, 33% Small

#### What Do G2 Reviewers Say About Confluent?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Einfachheit und Skalierbarkeit** der Cloud-Dienste von Confluent, die ihre Erfahrung mit Kafka und Flink verbessern.
- Benutzer schätzen die **mühelose Echtzeit-Datenintegration** durch Confluents verwaltete Cloud-Dienste, die ihren Arbeitsablauf erheblich verbessern.
- Benutzer schätzen die **große Auswahl an Konnektoren** in Confluent, die die Echtzeit-Datenintegration vereinfachen und die Produktivität steigern.
- Benutzer schätzen die **vereinfachte Echtzeit-Datenintegration** mit Confluent und profitieren von seinen verwalteten Cloud-Diensten und umfangreichen Konnektoren.
- Benutzer schätzen die **Benutzerfreundlichkeit** von Confluent, wodurch die Datenintegration und Stream-Verarbeitung mühelos und effizient wird.

##### Cons

- Benutzer bemerken, dass die **Kostenschätzung hoch sein kann** , wenn das Datenvolumen zunimmt, was Zeit erfordert, um das System zu erlernen.
- Benutzer finden Confluent **teuer** , da die Kosten mit dem Datenvolumen steigen und die Funktionen in niedrigeren Editionen begrenzt sind.
- Benutzer stehen bei Confluent vor einer **steilen Lernkurve** , zusammen mit steigenden Kosten, wenn das Datenvolumen zunimmt.
- Benutzer finden einen **Mangel an Funktionen** in Confluent, insbesondere da wesentliche Werkzeuge auf die Enterprise-Edition beschränkt sind.
- Benutzer finden die **steile Lernkurve** herausfordernd, da sie erhebliche Zeit benötigen, um den Workflow und die Funktionen von Confluent zu verstehen.

#### What Are Recent G2 Reviews of Confluent?

**["Mühelose Kafka-Verwaltung mit Confluent"](https://www.g2.com/de/survey_responses/confluent-review-12744384)**

**Rating:** 4.5/5.0 stars

_— Abhishek g._

[Read full review](https://www.g2.com/de/survey_responses/confluent-review-12744384)

**["nahtloses Erlebnis"](https://www.g2.com/de/survey_responses/confluent-review-8457785)**

**Rating:** 5.0/5.0 stars

_— Anup M._

[Read full review](https://www.g2.com/de/survey_responses/confluent-review-8457785)

#### What Are G2 Users Discussing About Confluent?

- [Was ist Ihr primärer Anwendungsfall für Confluent und wie verbessert es Ihr Echtzeit-Datenstreaming?](https://www.g2.com/de/discussions/what-is-your-primary-use-case-for-confluent-and-how-does-it-enhance-your-real-time-data-streaming) - 1 upvote
- [What is Confluent product?](https://www.g2.com/de/discussions/what-is-confluent-product)
- [What does Confluent software do?](https://www.g2.com/de/discussions/what-does-confluent-software-do)
- [What is the difference between Confluent and Kafka?](https://www.g2.com/de/discussions/what-is-the-difference-between-confluent-and-kafka)
- [Is Confluent SaaS or PaaS?](https://www.g2.com/de/discussions/is-confluent-saas-or-paas)

### [Starburst](https://www.g2.com/products/starburst/reviews)

Starburst is the data platform for analytics, applications, and AI, unifying data across clouds and on-premises to accelerate AI innovation. Organizations—from startups to Fortune 500 enterprises in 60+ countries—rely on Starburst for fast data access, seamless collaboration, and enterprise-grade governance on an open hybrid data lakehouse. Wherever data lives, Starburst unlocks its full potential, powering data and AI from development to deployment. By future-proofing data architecture, Starburst helps businesses fuel innovation with AI. Learn more at starburst.ai

**Average Rating:** 4.4/5.0

**Total Reviews:** 102

#### How Do G2 Users Rate Starburst?

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.0/10 (Category avg: 8.5/10)
- **Data Workflow:** 7.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Starburst?

- **Seller:** [Starburst](https://www.g2.com/sellers/starburst)
- **Company Website:** www.starburst.io
- **Year Founded:** 2017
- **HQ Location:** Boston, MA
- **Twitter:** @starburstdata  
3,454 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=6e8e7115417f25433cd515a6e146b579fb0353f1130e9be509f37e24c1adb7f6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fstarburstdata%2F&secure%5Burl_type%5D=linkedin_company_website)  
539 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 45% Large, 33% Small

#### What Do G2 Reviewers Say About Starburst?

_AI-generated summary from verified user reviews_

##### Pros

- Users highlight the **fast querying** capabilities of Starburst, enabling quick data access and analysis across various sources.
- Users appreciate the **query efficiency** of Starburst, allowing quick access to diverse data sources effortlessly.
- Users value the **seamless integration** with various data sources, significantly enhancing data access and analysis efficiency.
- Users appreciate the **ease of use** of Starburst, enabling efficient data access and real-time analysis from various sources.
- Users commend Starburst for its **superior performance** , rapidly retrieving accurate data and enhancing overall productivity.

##### Cons

- Users often experience **query issues** with Starburst, finding it less efficient for complex queries compared to traditional SQL editors.
- Users find the **initial setup complexity** of Starburst challenging, especially with configuration and optimizing queries.
- Users note a **steep learning curve** when setting up Starburst, making onboarding and optimization challenging for new users.
- Users often experience **slow performance** on Starburst, especially during peak usage and with complex queries.
- Users report **performance issues** with Starburst, especially when executing complex queries, causing significant slowdowns.

#### What Are Recent G2 Reviews of Starburst?

**["Starburst Enterprise Review"](https://www.g2.com/survey_responses/starburst-review-10604384)**

**Rating:** 4.0/5.0 stars

_— Rajiv B._

[Read full review](https://www.g2.com/survey_responses/starburst-review-10604384)

**["Powerful Data Virtualization Platform for Unified Analytics and AI-Driven Insights"](https://www.g2.com/survey_responses/starburst-review-13207252)**

**Rating:** 4.5/5.0 stars

_— Suryansh S._

[Read full review](https://www.g2.com/survey_responses/starburst-review-13207252)

#### What Are G2 Users Discussing About Starburst?

- [What does Starburst do?](https://www.g2.com/discussions/what-does-starburst-do)
- [What is Starburst Presto?](https://www.g2.com/discussions/what-is-starburst-presto)
- [What is Starburst tech?](https://www.g2.com/discussions/what-is-starburst-tech)
- [What does Starburst data do?](https://www.g2.com/discussions/what-does-starburst-data-do)

### [Azure Data Lake Analytics](https://www.g2.com/fr/products/azure-data-lake-analytics/reviews)

Azure Data Lake Analytics est une architecture de traitement de données distribuée et basée sur le cloud, proposée par Microsoft dans le cloud Azure. Elle est basée sur YARN, tout comme la plateforme open-source Hadoop.

**Average Rating:** 4.2/5.0

**Total Reviews:** 28

#### How Do G2 Users Rate Azure Data Lake Analytics?

- **the product a-t-il été un bon partenaire commercial?:** 8.6/10 (Category avg: 8.9/10)
- **Analyse multi-sources:** 7.9/10 (Category avg: 8.5/10)
- **Analyse en temps réel:** 8.1/10 (Category avg: 8.5/10)
- **Flux de travail de données:** 8.5/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Data Lake Analytics?

- **Vendeur:** [Microsoft](https://www.g2.com/fr/sellers/microsoft)
- **Année de fondation:** 1975
- **Emplacement du siège social:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 abonnés Twitter
- **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employés sur LinkedIn®
- **Propriété:** MSFT

#### Who Uses This Product?

- **Top Industries:** Technologie de l'information et services, Logiciels informatiques
- **Company Size:** 54% Large, 27% Medium

#### What Are Recent G2 Reviews of Azure Data Lake Analytics?

**["La centrale de gestion des données"](https://www.g2.com/fr/survey_responses/azure-data-lake-analytics-review-7674920)**

**Rating:** 5.0/5.0 stars

_— Sam L._

[Read full review](https://www.g2.com/fr/survey_responses/azure-data-lake-analytics-review-7674920)

**["Excellent service pour gérer vos besoins en big data"](https://www.g2.com/fr/survey_responses/azure-data-lake-analytics-review-5253914)**

**Rating:** 4.5/5.0 stars

_— Abhishek C._

[Read full review](https://www.g2.com/fr/survey_responses/azure-data-lake-analytics-review-5253914)

#### What Are G2 Users Discussing About Azure Data Lake Analytics?

- [À quoi sert Azure Data Lake Analytics ?](https://www.g2.com/fr/discussions/what-is-azure-data-lake-analytics-used-for)
- [How do I make Azure Data Lake Analytics?](https://www.g2.com/fr/discussions/how-do-i-make-azure-data-lake-analytics)
- [What is Azure Data lake and stream analytics tools?](https://www.g2.com/fr/discussions/what-is-azure-data-lake-and-stream-analytics-tools)
- [What are the key capabilities of Microsoft Azure Data Lake Analytics?](https://www.g2.com/fr/discussions/what-are-the-key-capabilities-of-microsoft-azure-data-lake-analytics)
- [What is Azure Data Lake Analytics?](https://www.g2.com/fr/discussions/what-is-azure-data-lake-analytics)

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[Browse Big Data Analytics Themes](/categories/big-data-analytics/themes)

 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Big data analytics software provides insights into large, complex data sets collected from big data clusters, helping business users understand data trends, patterns, and anomalies through visualizations, reports, and dashboards, often requiring query languages to extract data from unstructured file systems.

### Core Capabilities of Big Data Analytics Software

To qualify for inclusion in the Big Data Analytics category, a product must:

- Consume data, query file systems, and connect directly to big data clusters
- Allow users to prepare complex big data sets into helpful and understandable data visualizations
- Create business-applicable reports, visualizations, and dashboards based on discoveries inside the data sets

### Common Use Cases for Big Data Analytics Software

Data engineers, analysts, and business intelligence teams use big data analytics software to extract value from large-scale, unstructured data environments. Common use cases include:

- Querying and analyzing large Hadoop or distributed data clusters to surface business insights
- Detecting patterns and anomalies in high-volume data sets for operational or strategic decision-making
- Building self-service charts and dashboards for non-technical stakeholders from big data sources

### How Big Data Analytics Software Differs from Other Tools

Big data analytics software is solely focused on manipulating complex, large-scale data clusters into understandable visualizations, differentiating it from [analytics platforms](https://www.g2.com/categories/analytics-platforms), which support a wide range of data sources and connectors beyond big data. The two categories are mutually exclusive. Big data analytics tools are commonly used at companies running Hadoop in conjunction with [big data processing and distribution software](https://www.g2.com/categories/big-data-processing-and-distribution) and integrate with [data warehouse software](https://www.g2.com/categories/data-warehouse) as the central hub for integrated data. Some solutions also leverage [machine learning](https://www.g2.com/categories/machine-learning) and [natural language processing](https://www.g2.com/categories/natural-language-processing-nlp) to enable natural language querying.

### Insights from G2 on Big Data Analytics Software

Based on category trends on G2, query flexibility and scalability for large data sets stand out as standout capabilities. Faster insight generation from complex data environments stand out as the primary benefit of adoption.

Top Tools at a Glance

| Product | Best for | User Review |
| --- | --- | --- |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_a6c205d533dba77b318af96d91beb2ac/databricks.jpeg "Product Avatar Image")](https://www.g2.com/products/databricks/reviews)[Databricks](https://www.g2.com/products/databricks/reviews)[4.6/5(1,363)](https://www.g2.com/products/databricks/reviews) | Unified lakehouse ETL, analytics, and ML pipelines | "Reliable Platform for Building Scalable Data Pipelines" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_96b275379465d759df5bffd0099d849a/google-cloud-bigquery.png "Product Avatar Image")](https://www.g2.com/products/google-cloud-bigquery/reviews)[BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)[4.5/5(1,224)](https://www.g2.com/products/google-cloud-bigquery/reviews) | Serverless SQL analytics on petabyte-scale datasets | "Easy-to-Use Cloud Tool with Shareable, Saved Queries" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2b00e05c107c3273cea5264090c3c1d0/snowflake.jpg "Product Avatar Image")](https://www.g2.com/products/snowflake/reviews)[Snowflake](https://www.g2.com/products/snowflake/reviews)[4.5/5(763)](https://www.g2.com/products/snowflake/reviews) | Elastic multi-workload analytics with zero-infrastructure overhead | "Elastic Scaling and Fast Analytics with Snowflake" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_24bb2b0b5af8e7d875ea09d767bcb097/ibm-watsonx-data.jpg "Product Avatar Image")](https://www.g2.com/products/ibm-watsonx-data/reviews)[IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews)[4.4/5(172)](https://www.g2.com/products/ibm-watsonx-data/reviews) | Federated lakehouse querying across hybrid data environments | "Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_13aba37ae908d9cf29a2452925257ccc/azure-databricks.jpeg "Product Avatar Image")](https://www.g2.com/products/azure-databricks/reviews)[Azure Databricks](https://www.g2.com/products/azure-databricks/reviews)[4.5/5(239)](https://www.g2.com/products/azure-databricks/reviews) | Unified Spark-native lakehouse ETL and ML | "Balancing Performance and Complexity in Azure Databricks" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2c6c77d8284b2f6609c2f9dc0ba6b5a9/alteryx.png "Product Avatar Image")](https://www.g2.com/products/alteryx/reviews)[Alteryx](https://www.g2.com/products/alteryx/reviews)[4.6/5(892)](https://www.g2.com/products/alteryx/reviews) | No-code ETL and multi-source data blending | "Scales Operations and Saves Time with Automated Data Workflows" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_370c360364dc3d4f1f97e00cc7534fb5/kyvos-semantic-layer.png "Product Avatar Image")](https://www.g2.com/products/kyvos-semantic-layer/reviews)[Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews)[4.8/5(288)](https://www.g2.com/products/kyvos-semantic-layer/reviews) | Sub-second OLAP querying on cloud-scale datasets | "Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_756e21e5ff45db664431b3ea10f16115/azure-synapse-analytics.jpg "Product Avatar Image")](https://www.g2.com/products/azure-synapse-analytics/reviews)[Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews)[4.4/5(38)](https://www.g2.com/products/azure-synapse-analytics/reviews) | Unified ETL and big data warehousing on Azure | "Unified Data Warehousing and Big Data in One Powerful Platform" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_791b528c516cc1b08151fa6da3988161/dataiku.png "Product Avatar Image")](https://www.g2.com/products/dataiku/reviews)[Dataiku](https://www.g2.com/products/dataiku/reviews)[4.4/5(224)](https://www.g2.com/products/dataiku/reviews) | End-to-end ML pipelines with low-code collaboration | "Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_91bcb2c063fcfb0a82dfedcf1a6463d1/splunk-enterprise.jpg "Product Avatar Image")](https://www.g2.com/products/splunk-enterprise/reviews)[Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews)[4.3/5(434)](https://www.g2.com/products/splunk-enterprise/reviews) | Cross-source log correlation and security analytics | "Excellent Enterprise Observability and Log Management Solution for Hybrid Cloud Infrastructure" |

* * *

Show More

### Big Data Analytics Topics

- [What is Big Data Analytics Software?](#what-is-big-data-analytics-software)
- [What are the Common Features of Big Data Analytics Software?](#what-are-the-common-features-of-big-data-analytics-software)
- [What are the Benefits of Big Data Analytics Software?](#what-are-the-benefits-of-big-data-analytics-software)
- [Who Uses Big Data Analytics Software?](#who-uses-big-data-analytics-software)
- [What are the Alternatives to Big Data Analytics Software?](#what-are-the-alternatives-to-big-data-analytics-software)
- [Challenges with Big Data Analytics Software](#challenges-with-big-data-analytics-software)
- [Which Companies Should Buy Big Data Analytics Software?](#which-companies-should-buy-big-data-analytics-software)
- [How to Buy Big Data Analytics Software](#how-to-buy-big-data-analytics-software)
- [What Does Big Data Analytics Software Cost?](#what-does-big-data-analytics-software-cost)
- [Implementation of Big Data Analytics Software](#implementation-of-big-data-analytics-software)
- [Big Data Analytics Software Trends](#big-data-analytics-software-trends)

[
### Big Data Analytics Topics
Expand/Collapse ](#)
- [What is Big Data Analytics Software?](#what-is-big-data-analytics-software)
- [What are the Common Features of Big Data Analytics Software?](#what-are-the-common-features-of-big-data-analytics-software)
- [What are the Benefits of Big Data Analytics Software?](#what-are-the-benefits-of-big-data-analytics-software)
- [Who Uses Big Data Analytics Software?](#who-uses-big-data-analytics-software)
- [What are the Alternatives to Big Data Analytics Software?](#what-are-the-alternatives-to-big-data-analytics-software)
- [Challenges with Big Data Analytics Software](#challenges-with-big-data-analytics-software)
- [Which Companies Should Buy Big Data Analytics Software?](#which-companies-should-buy-big-data-analytics-software)
- [How to Buy Big Data Analytics Software](#how-to-buy-big-data-analytics-software)
- [What Does Big Data Analytics Software Cost?](#what-does-big-data-analytics-software-cost)
- [Implementation of Big Data Analytics Software](#implementation-of-big-data-analytics-software)
- [Big Data Analytics Software Trends](#big-data-analytics-software-trends)

## Learn More About Big Data Analytics Software

### What is Big Data Analytics Software?

The huge amount of data that is accessible to businesses today has made it a near necessity for them to implement some type of analytics software to better understand and act on that data. Implementing big data analytics software has been a major initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from big data clusters.

With the ability to visualize and understand business data, employees can make informed decisions. For example, retailers can use these tools to better understand inventory distribution across their channels and make data-driven decisions based on this data. Some big data analytics solutions may offer artificial intelligence or machine learning features, such as natural language processing, as an interface capability to further aid nontechnical users.

#### What Types of Big Data Analytics Software Exist?

Many types of big data analytics solutions share overlapping functionality, while simultaneously catering to different user personas such as data analysts and financial analysts or providing unique services.

Because of the unstructured nature of big data clusters, these analytics solutions require a query language to pull the data out of the file system. Most commercial table databases allow SQL queries; however, big data analytics tools do not necessarily offer such SQL language capabilities and may require a more intricate knowledge of querying from a data scientist. As an alternative, some solutions may offer self-service features so that the average employee can assemble their own charts and graphs from big data sets.

**Self-service big data analytics tools**

Self-service big data analytics tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other nondata team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. Similar to [analytics platforms](https://www.g2.com/categories/analytics-platforms), organizations use these tools to build interactive dashboards for discovering actionable insights. 

**Embedded analytics solutions**

Embedded analytics solutions offer the ability to integrate proprietary analytics functionality within other business applications. Commonly, businesses embed analytics solutions in software such as CRMs, ERP, and portals (e.g., intranets or extranets). Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision making. ** **

### What are the Common Features of Big Data Analytics Software?

Big data analytics software helps companies get a better understanding of their data. The following are some core features of this software: 

**Data connectivity:** If businesses cannot connect the requisite data, then there is no use for big data analytics software. The methods for connecting data include Hadoop and [Spark integration](https://www.g2.com/categories/big-data-analytics/f/spark-integration) which allows for processing and distribution workflows on top of Apache Hadoop and Apache Spark, respectively. In addition, this software should allow for analyzing data that is stored in [data lakes](https://www.g2.com/categories/big-data-analytics/f/data-lake), data warehouses, and data lake houses.

**Data transformation:** For data to be analyzed, it needs to be properly cleaned and transformed into a usable format. Big data analytics software provides features such as real-time analytics and data querying. With these features, businesses can gain a high-level view of their data in real time, allowing one to query it and better understand it. Through query languages like SQL, users can query their data and dig deeper into particular data sets and data points.

**Data operations:** Once the data is connected (or integrated) and transformed, it can be analyzed. Firstly, it is important to establish data workflows, which can help in stringing together specific functions and data sets to automate analytics iterations. In addition, big data analytics software provides the ability to visualize data through dashboards, as well as [notebooks](https://www.g2.com/categories/big-data-analytics/f/notebooks) which can be used to create visualization with predefined or scheduled queries. 

It is not always the case that one will access analytics via a standalone analytics platform. Therefore, some products provide [embedded analytics capabilities](https://www.g2.com/categories/big-data-analytics/f/embedded-analytics). This allows users to access analytics inside business applications, which allows for more streamlined work since the users need not switch between applications. 

Other Features of Big Data Analytics Software: [Governed Discovery](https://www.g2.com/categories/big-data-analytics/f/governed-discovery),

### What are the Benefits of Big Data Analytics Software?

Data is both common and invaluable and within that data lies insights that could impact an organization's processes and performance. There are seemingly infinite insights a business can pull from their data and numerous reasons to utilize big data analytics software. 

Big data analytics software helps people make decisions easier by allowing teams to gain deeper insight into their data. With increased data literacy, teams across a business, from sales to marketing to finance can become more efficient and better understand how they can improve through data-driven initiatives. 

With big data analytics software, businesses can ingest, integrate, and prepare big data sources. Subsequently, they can connect all company data sources into a single platform to make cross-department connections, visualize and understand company data, encourage data-driven decision making for business optimization, and discover new insights that can enhance the bottom line.

**Enable data-driven decision making:** Businesses can use big data analytics software to fuel digital transformation by leveraging data to drive business decisions. Companies can leverage analytics and business intelligence (BI) tools to understand all aspects of the business, including hiring forecasts, which marketing campaign should be used to target certain demographics, which sales prospects to target first, supply chain optimization, and many others.

**Measure and understand company performance:** Organizations often leverage data visualization tools to track company key performance indicators (KPIs) in real time. From there, big data analytics software can be used to determine why the business is either exceeding or falling short of those important company metrics. When stakeholders develop a keen understanding of why the business is performing the way it is, they can make adjustments and pivots; if a team is falling short of a goal, they can examine and adjust processes as needed. It is one thing to simply know the performance of sales or web traffic numbers, but it is another to dig into the reasons behind it and adapt based on what is successful and what is not.

**Discover new actionable insights:** Analytics tools combine data from a variety of sources, including [accounting software](https://www.g2.com/categories/accounting), [enterprise resource planning (ERP) software](https://www.g2.com/categories/erp), [CRM software](https://www.g2.com/categories/crm),[marketing automation software](https://www.g2.com/categories/marketing-automation), and others. Data analysts can leverage this integrated data to find correlations between different departments, and their processes and actions, to discover previously hidden insights. For example, it is possible that certain sales tactics have varying impacts on the numbers for one specific product versus another. 

Analysts can discover this impact by comparing the list of closed accounts from their company CRM with products shipped in their ERP system. Teams are generally siloed and use disparate software, so these insights that were traditionally more difficult to discover, are now made easier. 

### Who Uses Big Data Analytics Software?

**Data analysts:** Depending on the complexity of the software, it is likely that analysts will be required. They can help set up the requisite queries, dashboards, and notebooks for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data.

**Operations and supply chain teams:** A company’s supply chain frequently has many touchpoints, and as a result, many data points. Therefore, employees working in operations and supply chain teams are able to use big data analytics software to gain a better understanding of their departments and the data that is generated, such as from an ERP system. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into this software, supply chain managers can optimize a number of processes to save time and resources.

**Finance teams:** Finance teams leverage big data analytics software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as [accounting software](https://www.g2.com/categories/accounting), employees such as chief financial officers (CFOs) can see how well the business is performing. As mentioned above, these employees will likely be accessing the software via self-service dashboards that were set up by data analysts. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools.

**Sales and marketing teams:** Sales teams also seek to improve financial metrics and can benefit tremendously from being more data-driven. Through the use of both self-service analytics tools and embedded analytics solutions, they can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue.

For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, analytics tools allow marketing teams to track the performance of those campaigns in one central location.

**Consultants:** Businesses do not always have the luxury to build, develop, and optimize their own analytics solutions. Some businesses opt to employ external consultants, such as [business intelligence (BI) consulting providers](https://www.g2.com/categories/business-intelligence-bi-consulting). These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields. 

### What are the Alternatives to Big Data Analytics Software?

Alternatives to big data analytics software can replace this type of software, either partially or completely:

[Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** Analytics platforms might include big data integrations, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.

[Log analysis software](https://www.g2.com/categories/log-analysis): Businesses that are focused on log data might benefit from deploying log analysis software, which is used to analyze log data from applications and systems. It should be kept in mind that this software is much more limited in terms of data types and data sources to which it can be connected to. However, since log analysis software focuses on logs, it frequently provides more granular details around log-related data.

[Stream analytics software](https://www.g2.com/categories/stream-analytics) **:** When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with internet of things (IoT) data, which one frequently wants to analyze in real time.

[Predictive analytics software](https://www.g2.com/categories/predictive-analytics): Broad-purpose big data analytics software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more finetuned solution. 

[Text analysis software](https://www.g2.com/categories/text-analysis): Big data analytics software is focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.

#### Software Related to Big Data Analytics Software

Related solutions that can be used together with big data analytics software include:

[Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** A key solution necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using BI tools. Some BI platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool.

### Challenges with Big Data Analytics Software

Software solutions can come with their own set of challenges. 

**Need for skilled employees:** Big data analytics software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data. 

Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house.

**Data organization:** To get the most of analytics solutions, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location. 

Businesses may need to purchase a dedicated [data preparation software](https://www.g2.com/categories/data-preparation) as well to ensure that data is joined and is clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis.

**User adoption:** It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.

### Which Companies Should Buy Big Data Analytics Software?

As has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and across industries should consider some sort of analytics solution, such as big data analytics in order to make sense of that data and begin to make data-driven decisions. 

**Financial services:** Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes.

**Healthcare:** Within the space of healthcare, bad data practices might have dire or even deadly consequences. Big data analytics software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter.

**Retail** : Retail organizations, whether they be B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, in order to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With big data analytics software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions.

### How to Buy Big Data Analytics Software

#### Requirements Gathering (RFI/RFP) for Big Data Analytics Software

If a company is just starting out on their analytics journey, g2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2 is a great place to sort and filter reviews based on these criteria, along with many more.

As mentioned above, the variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs, as well as their future needs as they accumulate more data. 

To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a big data analytics software.

#### Compare Big Data Analytics Software Products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition. 

#### Selection of Big Data Analytics Software

**Choose a selection team**

As big data analytics software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.

**Negotiation**

Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### What Does Big Data Analytics Software Cost?

Businesses decide to deploy big data analytics software with the goal of deriving some degree of a return on investment (ROI).

#### Return on Investment (ROI)

As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the big data analytics tool.

### Implementation of Big Data Analytics Software

**How is Big Data Analytics Software Implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

**Who is Responsible for Big Data Analytics Software Implementation?**

It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together their data and begin the journey of analytics, starting with proper data preparation and management.

### Big Data Analytics Software Trends

**Data literacy**

Business data is no longer locked up in silos. With big data analytics solutions, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) software](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data easier and more powerful, providing more accurate results.

Implementing analytics software has been a major initiative for companies undergoing digital transformation as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from all their various sources.

**Shift to the cloud**

The move from on-premises data analytics to the cloud has been underway for a number of years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insights. The move away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access. 

**Conversational AI**

Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they are looking for using intuitive language. Intuitive methods of querying data mean enabling a larger user base to access and make sense of company data.

**Machine learning**

AI is quickly becoming a promising feature of analytics solutions throughout the whole data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, big data analytics solutions are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and helping them to understand what they are seeing.