What is Data Fusion?

Data Fusion is a fully managed, cloud-native, and flexible enterprise data integration platform offered by Google. It is designed for quickly developing and managing data pipelines.
With organizations dealing with massive amounts of varying data, Data Fusion helps bring it together, easing the application of data analytics. This platform can bring social, machine, or transactional data in different formats from applications, messaging systems, and databases, to mention a few. Also, it provides an intuitive visual interface, enabling you to visualize your data.
Additionally, Data Fusion offers advanced deployment capabilities. This enables you to execute data pipelines on popular managed cloud services such as Dataproc and Ephemeral. Even better, this enterprise integration platform is powered by an open-source CDAP, making the pipelines portable across GCP and hybrid/multi-cloud architectures. So, if you’re running on such cloud strategies, Data Fusion simplifies the deployment of your data pipelines across different cloud providers.

How Does Data Fusion Support Enterprise Data Integration?

Data integration is often a complex process. However, Data Fusion is optimized for integration, accelerating data transformation and analytics. Here are some of its integration capabilities:

• Connectors

Data Fusion supports over 200 data connectors and formats. This allows you to extract and merge data from different sources. Also, you can build data pipelines in a visual model-driven environment, helping improve productivity.

• Parsing and Enriching of Unstructured Data

Businesses often generate large volumes of unstructured data. This data is among the most challenging to integrate, analyze, and gain meaningful insights. However, Data Fusion offers capabilities to effectively parse and enrich unstructured data.

For instance, it uses Cloud artificial intelligence (AI) to convert audio to text. Also, it applies natural language processing (NLP) to extract features from documents and images and convert HL7 to FHIR. This helps ensure enterprise data integration processes comply with relevant regulatory standards.

• Data Wrangling Capabilities

Data Fusion offers advanced data wrangling capabilities. This enables you to seamlessly prepare and operationalize data. As a result, it improves business-IT collaboration, enhancing the overall outcome of your integration initiatives.

• Pre-Built APIs

APIs often act as the gateway to integrating data from different sources. The Data Fusion platform offers a library of built-in APIs, frameworks, and sensors. These features facilitate seamless enterprise data integration.

Also, it allows you to use the extensive REST API to develop, orchestrate, manage, and automate the lifecycle of your data pipelines. This streamlines enterprise data integration by consistently gathering, cleaning, and transforming data.

• Operational Analytics and Insights

As initially stated, enterprise data integration is a complex process. Many things can go wrong, resulting in significant losses. For instance, if data is not cleaned properly before being used for analytics, it can lead to misinformed decisions.

Data Fusion offers operational insights, enabling you to track data integration processes. Also, you can easily manage service-level agreements and optimize integration tasks based on these insights.

• Versatility

Data Fusion is a versatile data integration platform. It supports all data delivery modes, including real-time, streaming, and batch. This makes it a comprehensive platform for addressing streaming and batch-related use cases.

4 Benefits of Using Data Fusion in Enterprise Data Integration

Here are some reasons why you should consider Data Fusion in enterprise data integration processes:

1. Data Protection

Organizations often handle sensitive data. During integration, data can be exposed, leading to potential security breaches. However, Data Fusion offers various options for securing data, including the following:

• It allows you to store sensitive JDBC strings, URLs, and passwords in Cloud KMS and connect to external KMS systems.
• Data Fusion provides VPC Services Controls to protect data from exfiltration.
• This platform integrates with Cloud DLP, enabling you to encrypt, redact, and mask data in transit.
• It encrypts all data at rest either by default or through customer-managed encryption keys.
• Data Fusion offers a Private IP, facilitating secure access to on-premises data.

2. Data Consistency

Data consistency is a critical success factor in enterprise data integration. Data Fusion ensures data consistency in various ways, such as:

• Providing structured ways to specify transformations.
• Identifying changes in data formats and customizing error handling.
• Tracking data profiles to identify quality issues.

3. Modeling and Metadata

Data Fusion helps you gain insights into enterprise data integration processes with metadata. With Data Fusion, you can:

• Collect operational, business, and technical metadata for pipelines and datasets.
• Exchange metadata between integration and catalogs with end-user workbenches through REST APIs.
• Have a comprehensive data view, enabling you to understand profile data, relationships between datasets, and process flows.

4. It’s Open-Source

More organizations adopt hybrid/multi-cloud architectures to get the best out of each cloud platform. Data integration in such an environment is more challenging due to varying architectures. However, Data Fusion eases enterprise data integration in such settings.

It is a cloud-native and 100% open-source framework. Therefore, it allows you to build cloud-based and on-premises data analytics apps. You can execute and deploy integration pipelines across different environments without changing them to fit your business needs.

Final Thoughts

Data Fusion is a revolutionary enterprise data integration platform. It allows you to quickly and efficiently integrate data from different sources, create pipelines, and apply transformations. So, if you’re struggling to meet your data integration goals, it’s time to switch to Data Fusion. By embracing Data Fusion, you’ll make your data teams more productive and happier.

Also Read: Right Blend of ERP, CRM, Cloud, and Analytics Drives Digital Transformation

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Having the large amount of data that big data provides dumped into your organization’s systems is one thing, but being able to store, analyze and visualize that data in real-time is something else entirely. More and more organizations need to have real-time information to fully understand what is happening within their organization.

What are the benefits and drawbacks you can expect from processing this real-time data from big data?

Advantages of real-time data analysis from big data

Organizations capable of processing data in real-time find that their investment has paid off when they experience situations such as the following:

• IT is freed from an important workload: this translates into significant cost savings and a boost to the performance of the professionals in this Department, who instead of spending their time answering queries can deal with more important issues strategic. While implementing real-time data analysis tools can be expensive, over time it is synonymous with lower costs.
• Customer service is visibly improved: the monitoring that results from real-time data processing enables the organization to become proactive and prevent future errors and problems. In addition, it is easier for them to tailor their proposals to the real needs of the customer, who notices how their shopping experience improves, leading to an increase in sales and a boost to the loyalty index.
• The risk is minimized: processing and analyzing data in real-time makes it possible to identify any anomalies or errors instantly. Discovering and understanding these failures as they occur helps companies react quickly, thereby mitigating the effects of any operational problems and preventing their spread.
• Trends are more easily discovered: variations in demand are registered as soon as they are detected, promoting the necessary actions to avoid losing alignment with market developments. This valuable information is not translated into accurate decisions that guarantee adaptation to the client’s needs at all times.
• More advanced fraud detection: just as the appearance of various changes indicates the beginning of a trend, real-time data processing helps alert you to the signs that fraud is about to occur. The advantage is that this immediacy allows measures to be taken in time to stop the action or limit the damage that it could cause. Among the industries that have benefited the most from this capacity are organizations in the financial and insurance sectors.
• Competitor monitoring: Competitor analysis interpreted in its most advanced version, when carried out by processing data in real-time, is the only way to stay one step ahead of rival companies. The instantaneous knowledge of changes in their strategies or variations in their prices allows them to readjust their own, never losing their position in the market.
• Also Read: Data Strategy Trends For 2021 Everyone Need To Know

Challenges of big data real-time data analysis

While the benefits of real-time data analytics are very difficult to beat, especially when it comes to working with big data, it is also true that it is not within the reach of any organization.

In order to experience the advantages it provides, you must first be able to overcome challenges such as:

• Cultural changes: the use of knowledge in real-time requires a different way of working. It is necessary to introduce variations in processes and approach and to ensure that all business users are comfortable working in a data-driven organization, are prepared for it, and have the necessary tools.
• Investment in infrastructure: neither Hadoop nor many of the technology solutions on the market are ready to work with real-time data. It takes a special computing power in addition to certain attributes that are not so easy to find, nor are they within the budget of all companies.

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