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In keiner Weise ist eine internationale Vollzyklus-Softwareentwicklung das Unternehmen wurde 2007 gegründet. Wir sind ein Team von über 1800+ IT-Experten, die Software für andere entwickeln profis weltweit.
Über uns
Innowise ist ein internationales Unternehmen für den vollen Zyklus der Softwareentwicklung, welches 2007 gegründet wurde. Unser Team besteht aus mehr als 1600+ IT-Experten, welche Software für mehrere Branchen und Domänen weltweit entwickeln.

DataOps Dienstleistungen

Transform your data into a powerful asset that drives informed decisions and adapts to your evolving requirements with our DataOps services.

20+

DataOps projects

Transform your data into a powerful asset that drives informed decisions and adapts to your evolving requirements with our DataOps services.

20+

DataOps projects

  • Inefficient data engineering processes
  • Excessive manual work in data operations
  • Inconsistent data quality
  • Challenges in data security and compliance
  • Slow adaptability to changing business needs
  • Data engineering bottlenecks

Inefficient data engineering processes

Innowise’s team implements automated data pipelines with orchestration tools like Apache Airflow and Apache NiFi to enable consistent loading of data into target systems coming from different sources.

Inefficient data engineering processes

Excessive manual work in data operations

Through automation of repetitive tasks and the use of scripts and workflow management systems, we reduce manual effort, allowing teams to concentrate on more strategic activities.

Excessive manual work in data operations

Inconsistent data quality

We design frameworks that automate validation checks in the data quality process, which maintain accuracy, consistency, and completeness at all layers of the data pipeline.

Inconsistent data quality

Challenges in data security and compliance

Our experts protect sensitive data with encryption, enforce strict access controls, and conduct regular audits — all to prevent unauthorized access and provide adherence to regulations.

Challenges in Slow adaptability to changing business needs

Slow adaptability to changing business needs

To address slow responses to changes in business needs, we design flexible data architectures using cloud-based solutions like AWS or Azure — enabling rapid scalability and easy modifications.

Slow adaptability to changing business needs

Data engineering bottlenecks

Our approach includes establishing solid monitoring systems to track performance, holding training, and implementing continuous improvement practices through regular assessments.

Data engineering bottlenecks
Inefficient data engineering processes

Innowise’s team implements automated data pipelines with orchestration tools like Apache Airflow and Apache NiFi to enable consistent loading of data into target systems coming from different sources.

Inefficient data engineering processes
Excessive manual work in data operations

Through automation of repetitive tasks and the use of scripts and workflow management systems, we reduce manual effort, allowing teams to concentrate on more strategic activities.

Excessive manual work in data operations
Inconsistent data quality

We design frameworks that automate validation checks in the data quality process, which maintain accuracy, consistency, and completeness at all layers of the data pipeline.

Inconsistent data quality
Challenges in data security and compliance

Our experts protect sensitive data with encryption, enforce strict access controls, and conduct regular audits — all to prevent unauthorized access and provide adherence to regulations.

Challenges in Slow adaptability to changing business needs
Slow adaptability to changing business needs

To address slow responses to changes in business needs, we design flexible data architectures using cloud-based solutions like AWS or Azure — enabling rapid scalability and easy modifications.

Slow adaptability to changing business needs
Data engineering bottlenecks

Our approach includes establishing solid monitoring systems to track performance, holding training, and implementing continuous improvement practices through regular assessments.

Data engineering bottlenecks

Get comprehensive DataOps services

Our DataOps services focus on building efficient, scalable, and secure data environments — allowing businesses to make real-time decisions

We automate data workflows to minimize manual intervention and accelerate the delivery of valuable insights.

Our DataOps engineers apply cleaning, transformation, and synchronization techniques to guarantee data consistency throughout multiple sources.

While providing DataOps services, our team strategically implements checks and validations to maintain data accuracy and reliability.

We handle data governance by setting clear policies, managing metadata, providing access control, and maintaining data quality.

Datensicherheit und Compliance

Innowise guarantees adherence to industry standards like GDPR, HIPAA, and others — managing data handling to prevent breaches and guarantee legal conformity.

Our consulting experts create aligned custom strategies to help improve data accuracy, simplify processes, and speed up time-to-insight.

Make the most of your data with Innowise! We’re ready to make your data more reliable and accessible for analysis.

Industrien in denen wir arbeiten

  • Finanzen & Bankwesen
  • Gesundheitswesen
  • Einzelhandel und eCommerce
  • Telekommunikation
  • Manufacturing & supply chain
  • Energie & Versorgung
  • Automobile
  • Finanzdienstleistungen
  • Transportation & logistics

Finanzen & Bankwesen

DataOps supports banks and financial institutions in maintaining compliance with regulatory requirements by providing automated, auditable data trails.

  • Improved financial reporting and decision-making processes
  • Boosted fraud detection
  • Simplified compliance with financial regulations
Finanzen & Bankwesen

Gesundheitswesen

Managing sensitive patient data across various systems, complying with regulations, and using real-time analytics for improved patient care highlight the need for reliable DataOps strategies.

  • Real-time patient monitoring
  • Automated compliance and data security
  • A holistic, up-to-date view of each patient’s health
Gesundheitswesen

Einzelhandel und eCommerce

DataOps plays a key role in automating data integration across multiple channels, including online stores, POS systems, and customer touchpoints.

  • Personalized customer engagement
  • More targeted marketing strategies
  • Optimized inventory forecasting
Einzelhandel und eCommerce

Telekommunikation

With automated collecting and processing of data from different network elements, telecom companies can detect and resolve performance issues early.

  • Real-time monitoring and optimization of network performance
  • More tailored and responsive service offerings
  • Faster deployment of new services and network updates
Telekommunikation

Manufacturing & supply chain

By automating data workflows, well-crafted DataOps allow manufacturing & supply chain businesses to analyze production and inventory data effectively.

  • Improved production schedules and minimized downtime
  • Higher supply chain visibility and responsiveness
  • Optimized distribution and reduced consumption waste
Manufacturing & supply chain

Energie & Versorgung

Automated data pipelines allow energy and utility organizations to optimize resource allocation and predictive maintenance.

  • Improved resource allocation and consumption tracking
  • Faster response to outages and maintenance needs
  • Accurate and timely reporting through automated compliance reporting
Energie & Versorgung

Automobile

In the automotive industry, DataOps automates the flow of vehicle data to enable real-time diagnostics, helping manufacturers quickly identify and address performance issues.

  • Real-time diagnostics and performance monitoring
  • Reduced breakdowns and increased reliability
  • Improved customer insights
Automobile

Finanzdienstleistungen

Our DataOps services can automate data workflows, allowing insurers to process claims more efficiently and assess risk with greater accuracy.

  • Up-to-date risk models for faster underwriting decisions
  • Faster claims data processing
  • Personalized customer services
Finanzdienstleistungen

Transportation & logistics

DataOps helps integrate data from shipping companies, warehouses, and fleet management systems, providing real-time visibility into the movement of goods.

  • Higher delivery accuracy and reduced transit times
  • Reduced delays and mismanagement
  • Higher supply chain transparency
Transportation & logistics

Fallstudien

Google-Logo.
Hays-Logo.
PayPal-Logo.
Siemens-Logo.
Nike-Logo.
Volkswagen-Logo.
LVMH-Logo.
Nestle-Logo.
Novartis-Logo.
Spotify-Logo.
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Google-Logo.
Hays-Logo.
PayPal-Logo.
Siemens-Logo.
Nike-Logo.
Volkswagen-Logo.
LVMH-Logo.
Nestle-Logo.
Novartis-Logo.
Spotify-Logo.
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Our approach to DataOps services

In providing DataOps as a service, we adopt a collaborative approach — meaning we’re always open to discussions and ready to craft solutions for each demand that best fits the client’s current and strategic objectives.

  • Detailed project outline

We start with a clear project definition to make sure all stakeholders are aligned, preventing scope creep.

  • Precise cost estimation

Through strict risk assessments and realistic cost analyses, Innowise guarantees to maintain financial transparency from the start.

  • Kollaborativer Ansatz

Our experts build an environment where effective partnership and mutual respect for each participant are the cornerstones.

  • Qualitätssicherung

Quality control is paramount at every process stage — allowing us to identify and resolve issues early.

  • Solid data security

We employ encryption, controls for access, and continuous monitoring, enabling the safeguarding of sensitive information.

  • Hohe Skalierbarkeit

Our approach guarantees that as your data needs evolve, our systems can expand and adjust accordingly.

Choose Innowise as a DataOps consulting company

Innowise brings in only the top 3% of software engineers so that you can work with people who excel in their field. We continuously improve on what we know, and with more than 17 years of experience, our proficiency grows through each project we undertake. Let’s grow and thrive together!

Philip Tsikhanovich Leiter der Big Data-Abteilung bei Innowise

“Our DataOps services are all-encompassing. We automate, monitor, and optimize the scaling of your data pipelines to guarantee that no matter how complex your infrastructure is, there will always be speed and consistency in the data output. With modern tools and best practices, see how our team clears bottlenecks for smooth data integration, management, and delivery.”

Our DataOps process

Planung

Business, product, and engineering teams come together to define metrics and standards for data quality and availability.

Build-Management

Data engineers and data scientists create data products and machine learning models in this stage that will later power applications.

Die Integration

This is the process stage when code and the data product are integrated into an organization's overall tech stack.

Testen

Testing may include data integrity tests, completeness tests, and checking data compliance with business rules.

Releasing and deploying

This stage implies planning the release, conducting thorough testing, and employing CI/CD practices to automate the process.

Operating and monitoring

Data pipelines run continuously, so we use statistical process controls to monitor for anomalies and address them early.

Planung

Business, product, and engineering teams come together to define metrics and standards for data quality and availability.

Build-Management

Data engineers and data scientists create data products and machine learning models in this stage that will later power applications.

Die Integration

This is the process stage when code and the data product are integrated into an organization's overall tech stack.

Testen

Testing may include data integrity tests, completeness tests, and checking data compliance with business rules.

Releasing and deploying

This stage implies planning the release, conducting thorough testing, and employing CI/CD practices to automate the process.

Operating and monitoring

Data pipelines run continuously, so we use statistical process controls to monitor for anomalies and address them early.

Planung

Business, product, and engineering teams come together to define metrics and standards for data quality and availability.

Build-Management

Data engineers and data scientists create data products and machine learning models in this stage that will later power applications.

Die Integration

This is the process stage when code and the data product are integrated into an organization's overall tech stack.

Testen

Testing may include data integrity tests, completeness tests, and checking data compliance with business rules.

Releasing and deploying

This stage implies planning the release, conducting thorough testing, and employing CI/CD practices to automate the process.

Operating and monitoring

Data pipelines run continuously, so we use statistical process controls to monitor for anomalies and address them early.

Want to transform your data processes? Our DataOps team can help you achieve improved data quality, faster data delivery, better collaboration between teams, and other measurable benefits.
Want to transform your data processes? Our DataOps team can help you achieve improved data quality, faster data delivery, better collaboration between teams, and other measurable benefits.

Core DataOps technologies we work with

Data pipeline automation
  • Apache Airflow
  • Luigi
  • Prefect
  • Kubeflow Pipelines
  • Dagster
Datenintegration
  • Apache Nifi
  • Talend
  • Fivetran
  • Stitch
  • Informatica PowerCenter
  • Apache Kafka
Datenqualitätsmanagement
  • Great Expectations
  • Deequ
  • Talend Data Quality
  • Ataccama
  • Datafold
Datenverwaltung
  • Collibra
  • Alation
  • Informatica Axon
  • Apache Atlas
  • Microsoft Purview
Continuous data delivery
  • Apache Kafka
  • Debezium
  • Google Cloud Dataflow
  • Apache Flink
  • Confluent
Datensicherheit und Compliance
  • AWS KMS
  • Apache Ranger
  • Snowflake Security Features
  • Databricks Data Governance
  • Okta
  • OneTrust
  • BigID
  • HashiCorp Vault
DataOps strategy and consulting
  • DataKitchen
  • Unravel
  • StreamSets
  • Cognizant
  • Wipro
  • Tata Consultancy Services
    (TCS)

Anerkannt von den Besten
unter den Besten

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Wählen Sie Ihr Preismodell

Fixed-price

This option means the price is agreed upon and calculated based on the anticipated time and effort required. You pay a set amount for a defined scope of work, getting predictability. However, it provides limited flexibility for changes throughout the project.

Zeit und Material

This option means you pay for our team’s actual hours worked. The cost varies based on the time spent and the specialists involved. This approach enables adjustments during the project, with additional hours charged as needed.

Was unsere Kunden sagen

Egzon Gajtani Koordinator strategischer Projekte Tangoo Professional Network
Firmenlogo

"Wir waren mit dem Ergebnis des Projekts und den Leistungen von Innowise sehr zufrieden. Sie waren sehr reaktionsschnell und haben rechtzeitig kommuniziert, was eine reibungslose und effiziente Zusammenarbeit ermöglichte."

  • Industrie IT-Services
  • Teamgröße 2 Spezialisten
  • Projektdauer 6 Monate
  • Dienste Personalaufstockung
Joakim Rosen Hauptentwickler YouWish AS
Firmenlogo

“Innowise hat viele Projekte abgeschlossen und erfüllt seine Aufgaben durchweg gut. Ihr ergebnisorientierter Ansatz ermöglicht es ihnen, ihre Bemühungen je nach den erforderlichen Ergebnissen schnell zu skalieren.”

  • Industrie Verbraucherprodukte
  • Teamgröße 4 Spezialisten
  • Projektdauer 28+ Monate
  • Dienste Personalaufstockung
Gian Luca De Bonis CEO & CTO Enable Development OÜ
Firmenlogo

Wir sind beeindruckt von ihrer Flexibilität und ihrer Bereitschaft, Lösungen für herausfordernde Situationen zu finden. Sie haben in jeder Situation aktiv geholfen. Die Bereitschaft des Teams, optimale Ergebnisse zu liefern, sichert den Erfolg der Partnerschaft.

  • Industrie IT consulting
  • Teamgröße 8
  • Projektdauer 36 Monate
  • Dienste Personalaufstockung

FAQ

What's the difference between DataOps and DevOps?

They differ in the areas they target: DataOps targets data processes, while DevOps targets software delivery. DataOps is all about automating data pipelines and continuous integration to increase efficiency and quality in data management and analytics. DevOps, on the other hand, amplifies the collaboration between software development and operation to deliver software reliably.

What's the difference between DataOps vs MLOps?

Both methodologies are designed to improve collaboration, efficiency, and quality, but they target different aspects of data and machine learning workflows. While DataOps focuses on the data lifecycle and analytics processes, MLOps covers the model deployment and operation aspects of machine learning.

Can you integrate DataOps with our existing data tools and platforms?

Certainly! You just have to get in touch with us, and we will work with you to closely evaluate your existing systems and identify the ways and means to optimize them. We guarantee a frictionless integration effort to maximize your data workflows and improve collaboration across your teams. Let’s get started!

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    Warum Innowise?

    1800+

    IT-Fachleute

    93%

    wiederkehrende Kunden

    17+

    Jahre Expertenerfahrung

    1100+

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