Data Integration – 🔴 Red Button Data

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Apr 16 2024

Overcoming Real-Time Data Integration Challenges to Optimize for Surgical Capacity

In the healthcare industry, surgical capacity management is one of the biggest issues organizations face. Hospitals and surgery centers must be efficient in handling their resources. The margins are too small for waste, and there are too many patients in need of care. Data, particularly real-time data, is an essential asset. But it is only […]

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Actian Platform Receives Data Breakthrough Award for Innovative Integration Capabilities

Data integration is a critical capability for any organization looking to connect their data—in an era when there’s more data from more sources than ever before. In fact, data integration is the key to unlocking and sustaining business growth. A modern approach to data integration elevates analytics and enables richer, more contextual insights by bringing together large data sets from new and existing sources.

That’s why you need a data platform that makes integration easy. And the Actian Data Platform does exactly that. It’s why the platform was recently honored with the prestigious “Data Integration Solution of the Year” award from Data Breakthrough. The Data Breakthrough Aware program recognizes the top companies, technologies, and products in the global data technology market.

Whether you want to connect data from cloud-based sources or use data that’s on-premises, the integration process should be simple, even for those without advanced coding or data engineering skill sets. Ease of integration allows business analysts, other data users, and data-driven applications to quickly access the data they need, which reduces time to value and promotes a data-driven culture.

Access the Autonomy of Self-Service Data Integration

Being recognized by Data Breakthrough, an independent market intelligence organization, at its 5th annual awards program highlights the Actian platform’s innovative capabilities for data integration and our comprehensive approach to data management. With the platform’s modern API-first integration capabilities, organizations in any industry can connect and leverage data from diverse sources to build a more cohesive and efficient data ecosystem.

The platform provides a unified experience for ingesting, transforming, analyzing, and storing data. It meets the demands of your modern business, whether you operate across cloud, on-premises, or in hybrid environments, while giving you full confidence in your data.

With the Actian platform, you can leverage a self-service data integration solution that addresses multiple use cases without requiring multiple products—one of the benefits that Data Breakthrough called out when giving us the award. The platform makes data easy to use for analysts and others across your organization, allowing you to unlock the full value of your data.

Making Data Integration Easy

The Actian Data Platform offers integration as a service while making data integration, data quality, and data preparation easier than you may have ever thought possible. The recently enhanced platform also assists in lowering costs and actively contributes to better decision making across the business.

The Actian platform is unique in its ability to collect, manage, and analyze data in real time with its transactional database, data integration, data quality, and data warehouse capabilities. It manages data from any public cloud, multi or hybrid cloud, and on-premises environments through a single pane of glass.

All of this innovation will be increasingly needed as more organizations—more than 75% of enterprises by 2025—will have their data in data centers across multiple cloud providers and on-premises. Having data in various places requires a strategic investment in data management products that can span multiple locations and have the ability to bring the data together.

This is another area where the Actian Data Platform delivers value. It lets you connect data from all your sources and from any environment to break through data silos and streamline data workflows, making trusted data more accessible for all users and applications.

Try the Award-Winning Platform With a Guided Experience

The Actian Data Platform also enables you to prep your data to ensure it’s ready for AI and also help you use your data to train AI models effectively. The platform can automate time-consuming data preparation tasks, such as aggregating data, handling missing values, and standardizing data from various sources.

One of our platform’s greatest strengths is its extreme performance. It offers a nine times faster speed advantage and 16 times better cost savings over alternative platforms. We’ve also made recent updates to improve user friendliness. In addition to using pre-built connectors, you can easily connect data and applications using REST- and SOAP-based APIs that can be configured with just a few clicks.

Are you interested in experiencing the platform for yourself? If so, we invite you to participate in a guided free trial. For a limited time, we’re offering a 30-day trial with our team of technical experts. With your data and our expertise, you’ll see firsthand how the platform lets you go from data source to decision quickly and with full confidence.

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The Importance of Effective Data Integration and Automation in Today’s Digital Landscape

In today’s data-driven world, the demand for seamless data integration and automation has never been greater. Various sectors rely heavily on data and applications to drive their operations, making it crucial to have efficient methods of integrating and automating processes. However, ensuring successful implementation requires careful planning and consideration of various factors.

Data integration refers to combining data from different sources and systems into a unified, standardized view. This integration gives organizations a comprehensive and accurate understanding of their data, enabling them to make well-informed decisions. By integrating data from various systems and applications, companies can avoid inconsistencies and fragmentations often arising from siloed data. This, in turn, leads to improved efficiency and productivity across the organization.

One of the primary challenges in data integration is the complexity and high cost associated with traditional system integration methods. However, advancements in technology have led to the availability of several solutions aimed at simplifying the integration process. Whether it’s in-house development or leveraging third-party solutions, choosing the right integration approach is crucial for achieving success. IT leaders, application managers, data engineers, and data architects play vital roles in this planning process, ensuring that the chosen integration approach aligns with the organization’s goals and objectives.

Before embarking on an integration project, thorough planning and assessment are essential. Understanding the specific business problems that need to be resolved through integration is paramount. This involves identifying the stakeholders and their requirements and the anticipated benefits of the integration. Evaluating different integration options, opportunities, and limitations is also critical. Infrastructure costs, deployment, maintenance efforts, and the solution’s adaptability to future business needs should be thoroughly considered before deciding on an integration approach.

Five Foundational Areas For Initiating Any Data Integration Project

Establishing the Necessity: It is essential to understand the use cases and desired business outcomes to determine the necessity for an integration solution.
Tailoring User Experience: The integration solution should provide a unique user experience tailored to all integration roles and stakeholders.
Understanding Existing Business Systems and Processes: A detailed understanding of the existing business systems, data structures, scalability, dependencies, and regulatory compliance is essential.
Assessing Available Technologies: It is important to assess the available technologies and their potential to meet the organization’s integration needs and objectives.
Data Synchronization Management: Managing data synchronization is an ongoing process that requires careful planning, ownership, management, scheduling, and control.

Effective data integration and automation are crucial for organizations to thrive in today’s data-driven world. With the increasing demand for data and applications, it is imperative to prevent inconsistencies and fragmentations. By understanding the need for integration, addressing foundational areas, and leveraging solutions like Actian, organizations can streamline their data integration processes, make informed decisions, and achieve their business objectives. Embracing the power of data integration and automation will pave the way for future success in the digital age.

If you would like more information on how to get started with your next integration project, download our free eBook: 5 Planning Considerations for Successful Integration Projects.

A Solution for Seamless Data Integration

Actian offers a suite of solutions to address the challenges associated with integration. Their comprehensive suite of products covers the entire data journey from edge to cloud, ensuring seamless integration across platforms. The Actian platform provides the flexibility to meet diverse business needs, empowering companies to effectively overcome data integration challenges and achieve their business goals. By simplifying how individuals connect, manage, and analyze data, Actian’s data solutions facilitate data-driven decisions that accelerate business growth. The data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.

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The Data Engineering Decision Guide to Data Integration Tools

With organizations using an average of 130 apps, the problem of data fragmentation has become increasingly prevalent. As data production remains high, data engineers need a robust data integration strategy. A crucial part of this strategy is selecting the right data integration tool to unify siloed data.

Assessing Your Data Integration Needs

Before selecting a data integration tool, it’s crucial to understand your organization’s specific needs and data-driven initiatives, whether they involve improving customer experiences, optimizing operations, or generating insights for strategic decisions.

Understand Business Objectives

Begin by gaining a deep understanding of the organization’s business objectives and goals. This will provide context for the data integration requirements and help prioritize efforts accordingly. Collaborate with key stakeholders, including business analysts, data analysts, and decision-makers, to gather their input and requirements. Understand their data needs and use cases, including their specific data management rules, retention policies, and data privacy requirements.

Audit Data Sources

Next, identify all the sources of data within your organization. These may include databases, data lakes, cloud storage, SaaS applications, REST APIs, and even external data providers. Evaluate each data source based on factors such as data volume, data structure (structured, semi-structured, unstructured), data frequency (real-time, batch), data quality, and access methods (API, file transfer, direct database connection). Understanding the diversity of your data sources is essential in choosing a tool that can connect to and extract data from all of them.

Define Data Volume and Velocity

Consider the volume and velocity of data that your organization deals with. Are you handling terabytes of data per day, or is it just gigabytes? Determine the acceptable data latency for various use cases. Is the data streaming in real-time, or is it batch-oriented? Knowing this will help you select a tool to handle your specific data throughput.

Identify Transformation Requirements

Determine the extent of data transformation logic and preparation required to make the data usable for analytics or reporting. Some data integration tools offer extensive transformation capabilities, while others are more limited. Knowing your transformation needs will help you choose a tool that can provide a comprehensive set of transformation functions to clean, enrich, and structure data as needed.

Consider Integration with Data Warehouse and BI Tools

Consider the data warehouse, data lake, and analytical tools and platforms (e.g., BI tools, data visualization tools) that will consume the integrated data. Ensure that data pipelines are designed to support these tools seamlessly. Data engineers can establish a consistent and standardized way for analysts and line-of-business users to access and analyze data.

Choosing the Right Data Integration Approach

There are different approaches to data integration. Selecting the right one depends on your organization’s needs and existing infrastructure.

Batch vs. Real-Time Data Integration

Consider whether your organization requires batch processing or real-time data integration—they are two distinct approaches to moving and processing data. Batch processing is suitable for scenarios like historical data analysis where immediate insights are not critical and data updates can happen periodically, while real-time integration is essential for applications and use cases like Internet of Things (IoT) that demand up-to-the-minute data insights.

On-Premises vs. Cloud Integration

Determine whether your data integration needs are primarily on-premises or in the cloud. On-premises data integration involves managing data and infrastructure within an organization’s own data centers or physical facilities, whereas cloud data integration relies on cloud service providers’ infrastructure to store and process data. Some tools specialize in on-premises data integration, while others are built for the cloud or hybrid environments. Choose a tool that depends on factors such as data volume, scalability requirements, cost considerations, and data residency requirements.

Hybrid Integration

Many organizations have a hybrid infrastructure, with data both on-premises and in the cloud. Hybrid integration provides flexibility to scale resources as needed, using cloud resources for scalability while maintaining on-premises infrastructure for specific workloads. In such cases, consider a hybrid data integration and data quality tool like Actian’s DataConnect or the Actian Data Platform to seamlessly bridge both environments and ensure smooth data flow to support a variety of operational and analytical use cases.

Evaluating ETL Tool Features

As you evaluate ETL tools, consider the following features and capabilities:

Data Source and Destination Connectivity and Extensibility

Ensure that the tool can easily connect to your various data sources and destinations, including relational databases, SaaS applications, data warehouses, and data lakes. Native ETL connectors provide direct, seamless access to the latest version of data sources and destinations without the need for custom development. As data volumes grow, native connectors can often scale seamlessly, taking advantage of the underlying infrastructure’s capabilities. This ensures that data pipelines remain performant even with increasing data loads. If you have an outlier data source, look for a vendor that provides Import API, webhooks, or custom source development.

Scalability and Performance

Check if the tool can scale with your organization’s growing data needs. Performance is crucial, especially for large-scale data integration tasks. Inefficient data pipelines with high latency may result in underutilization of computational resources because systems may spend more time waiting for data than processing it. An ETL tool that supports parallel processing can handle large volumes of data efficiently. It can also scale easily to accommodate growing data needs. Data latency is a critical consideration for data engineers, because it directly impacts the timeliness, accuracy, and utility of data for analytics and decision-making.

Data Transformation Capabilities

Evaluate the tool’s data transformation capabilities to handle unique business rules. It should provide the necessary functions for cleaning, enriching, and structuring raw data to make it suitable for analysis, reporting, and other downstream applications. The specific transformations required can include: data deduplication, formatting, aggregation, normalization etc., depending on the nature of the data, the objectives of the data project, and the tools and technologies used in the data engineering pipeline.

Data Quality and Validation Capabilities

A robust monitoring and error-handling system is essential for tracking data quality over time. The tool should include data quality checks and validation mechanisms to ensure that incoming data meets predefined quality standards. This is essential for maintaining data integrity and accuracy, and it directly impacts the accuracy, reliability, and effectiveness of analytic initiatives. High quality data builds trust in analytical findings among stakeholders. When data is trustworthy, decision-makers are more likely to rely on the insights generated from analytics. Data quality is also an integral part of data governance practices.

Security and Regulatory Compliance

Ensure that the tool offers robust security features to protect your data during transit and at rest. Features such as SSH tunneling and VPNs provide encrypted communication channels, ensuring the confidentiality and integrity of data during transit. It should also help you comply with data privacy regulations, such as GDPR or HIPAA.

Ease of Use and Deployment

Consider the tool’s ease of use and deployment. A user-friendly low-code interface can boost productivity, save time, and reduce the learning curve for your team, especially for citizen integrators that can come from anywhere within the organization. A marketing manager, for example, may want to integrate web traffic, email marketing, ad platform, and customer relationship management (CRM) data into a data warehouse for attribution analysis.

Vendor Support

Assess the level of support, response times, and service-level agreements (SLAs) provided by the vendor. Do they offer comprehensive documentation, training resources, and responsive customer support? Additionally, consider the size and activity of the tool’s user community, which can be a valuable resource for troubleshooting and sharing best practices.

A fully managed hybrid solution like Actian simplifies complex data integration challenges and gives you the flexibility to adapt to evolving data integration needs.

The best way for data engineers to get started is to start a free trial of the Actian Data Platform. From there, they can load their own data and explore what’s possible within the platform. You can also book a demo to see how Actian can help automate data pipelines in a robust, scalable, price-performant way.

For a comprehensive guide to evaluating and selecting the right Data Integration tool, download the ebook Data Engineering Guide: Nine Steps to Select the Right Data Integration Tool.

The post The Data Engineering Decision Guide to Data Integration Tools appeared first on Actian.

Top Capabilities to Look for in Database Management Tools

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements.

The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data, while following governance policies and best practices. The tools can be grouped into categories based on their role, capabilities, or proprietary status.

For example, one category is open-source tools, such as PostgreSQL or pgAdmin. Another category is tools that manage an SQL infrastructure, such as Microsoft’s SQL Server Management Studio, while another is tools that manage extract, transform, and load (ETL) and extract, load, and transform (ELT) processes, such as those natively available from Actian.

Using a broad description, database management tools can ultimately include any tool that touches the data. This covers any tool that moves, ingests, or transforms data, or performs business intelligence or data analytics.

Data Management Tools for Modern Use Cases

Today’s data users require tools that meet a variety of needs. Some of the more common needs that are foundational to optimizing data and necessitate modern capabilities include:

Data management: This administrative and governance process allows you to acquire, validate, store, protect, and process data.
Data integration: Integration is the strategic practice of bringing together internal and external data from disparate sources into a unified platform.
Data migration: This entails moving data from its current or storage location to a new location, such as moving data between apps or from on-premises to the cloud.
Data transformation: Transformative processes change data from one format or structure into another for usage and ensure it’s cleansed, validated, and properly formatted.
Data modeling: Modeling encompasses creating conceptual, logical, and physical representations of data to ensure coherence, integrity, and efficiency in data management and utilization.
Data governance: Effective governance covers the policies, processes, and roles used to ensure data security, integrity, quality, and availability in a controlled, responsible way.
Data replication: Replicating data is the process of creating and storing multiple copies of data to ensure availability and protect the database against failures.
Data visualization: Visualizing data turns it into patterns and visual stories to show insights quickly and make them easily understandable.
Data analytics and business intelligence: These are the comprehensive and sophisticated processes that turn data into actionable insights.

It’s important to realize that needs can change over time as business priorities, data usage, and technologies evolve. That means a cutting-edge tool from 2020, for example, that offered new capabilities and reduced time to value may already be outdated by 2024. When using an existing tool, it’s important to implement new versions and upgrades as they become available.

You also want to ensure you continue to see a strong return on investment in your tools. If you’re not, it may make more sense from a productivity and cost perspective to switch to a new tool that better meets your needs.

Ease-of-Use and Integration Are Key

The mark of a good database management tool—and a good data platform—is the ability to ensure data is easy-to-use and readily accessible to everyone in the organization who needs it. Tools that make data processes, including analytics and business intelligence, more ubiquitous offer a much-needed benefit to data-driven organizations that want to encourage data usage for everyone, regardless of their skill level.

All database management tools should enable a broad set of users—allowing them to utilize data without relying on IT help. Another consideration is how well a tool integrates with your existing database, data platform, or data analytics ecosystem.

Many database management tool vendors and independent software vendors (ISVs) may have 20 to 30 developers and engineers on staff. These companies may provide only a single tool. Granted, that tool is probably very good at what it does, with the vendor offering professional services and various features for it. The downside is that the tool is not natively part of a data platform or larger data ecosystem, so integration is a must.

By contrast, tools that are provided by the database or platform vendor ensure seamless integration and streamline the number of vendors that are being used. You also want to use tools from vendors that regularly offer updates and new releases to deliver new or enhanced capabilities.

If you have a single data platform that offers the tools and interfaces you need, you can mitigate the potential friction that oftentimes exists when several different vendor technologies are brought together, but don’t easily integrate or share data. There’s also the danger of a small company going out of business and being unable to provide ongoing support, which is why using tools from large, established vendors can be a plus.

Expanding Data Management Use Cases

The goal of database management tools is to solve data problems and simplify data management, ideally with high performance and at a favorable cost. Some database management tools can perform several tasks by offering multiple capabilities, such as enabling data integration and data quality. Other tools have a single function.

Tools that can serve multiple use cases have an advantage over those that don’t, but that’s not the entire story. A tool that can perform a job faster than others, automate processes, and eliminate steps in a job that previously required manual intervention or IT help offers a clear advantage, even if it only handles a single use case. Stakeholders have to decide if the cost, performance, and usability of a single-purpose tool delivers a value that makes it a better choice than a multi-purpose tool.

Business users and data analysts often prefer the tools they’re familiar with and are sometimes reluctant to change, especially if there’s a long learning curve. Switching tools is a big decision that involves both cost and learning how to optimize the tool.

If you put yourself in the shoes of a chief data officer, you want to make sure the tool delivers strong value, integrates into and expands the current environment, meets the needs of internal users, and offers a compelling reason to make a change. You also should put yourself in the shoes of DBAs—does the tool help them do their job better and faster?

Delivering Data and Analytics Capabilities for Today’s Users

Tool choices can be influenced by no-code, low-code, and pro-code environments. For example, some data leaders may choose no- or low-code tools because they have small teams that don’t have the time or skill set needed to work with pro-code tools. Others may prefer the customization and flexibility options offered by pro-code tools.

A benefit of using the Actian Data Platform is that we offer database management tools to meet the needs of all types of users at all skill levels. We make it easy to integrate tools and access data. The Actian Platform offers no-code, low-code, and pro-code integration and transformation options. Plus, the unified platform’s native integration capabilities and data quality services feature a robust set of tools essential for data management and data preparation.

Plus, Actian has a robust partner ecosystem to deliver extended value with additional products, tools, and technologies. This gives customers flexibility in choosing tools and capabilities because Actian is not a single product company. Instead, we offer products and services to meet a growing range of data and analytics use cases for modern organizations.

Experience the Actian Data Platform for yourself. Take a free 30-day trial.

Related resources you may find useful:

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Choosing Tools for Data Pipeline Test Automation (Part 2)

In part one of this blog post, we described why there are many challenges for developers of data pipeline testing tools (complexities of technologies, large variety of data structures and formats, and the need to support diverse CI/CD pipelines). More than 15 distinct categories of test tools that pipeline developers need were described. Part two delves […]

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Data Management for a Hybrid World: Platform Components and Scalability

For most companies, a mixture of both on-premises and cloud environments called hybrid cloud is becoming the norm. This is the second blog in a two-part series describing data management strategies that businesses and IT need to be successful in their new hybrid cloud world. The previous post covered hybrid cloud data management, data residency, and compliance.

Platform Components

There are essential components for enabling hybrid cloud data analytics. First, you need data integration that can access data from all data sources. Your data integration tool needs a high degree of data quality management and transformation to convert raw data into a validated and usable format. Second, you should have the ability to orchestrate pipelines to coordinate and manage integration processes in a systematic and automated way. Third, you need a consistent data fabric layer that can be deployed across all environments and clouds to guarantee interoperability, consistency, and performance. The data fabric layer must have the ability to ingest different types of data as well. Last, you’ll need to transform data into formats and orchestrate pipelines.

Scaling Hybrid Cloud Investments

There are several costs to consider for hybrid cloud such as licensing, hardware, administration, and staff skill sets. Software as a Service (SaaS) and public cloud services tend to be subscription-based consumption models that are an Operational Expense (Opex). While on-premises and private cloud deployments are generally software licensing agreements that are a Capital Expenditure (Capex), subscription software models are great for starting small, but the costs can increase quickly. Alternatively, the upfront cost for traditional software is larger but your costs are generally fixed, pending growth.

Beyond software and licensing costs, scalability is a factor. Cloud services and SaaS offerings provide on-demand scale. Whereas on-premises deployments and products can also scale to a certain point, but eventually may require additional hardware (scale-up) and additional nodes (scale-out). Additionally, these deployments often need costly over-provisioning to meet peak demand.

For proprietary and high-risk data assets, leveraging on-premises deployments tends to be a consistent choice for obvious reasons. You have full control of managing the environment. It is worth noting that your technical staff needs to have strong security skills to protect on-premises data assets. On-premises environments rarely need infinite scale and sensitive data assets have minimal year-over-year growth. For low and medium-risk data assets, leveraging public cloud environments is quite common including multi-cloud topologies. Typically, these data assets are more varied in nature and larger in volume which makes them ideal for the cloud. You can leverage public cloud services and SaaS offerings to process, store, and query these assets. Utilizing multi-cloud strategies can provide additional benefits for higher SLA environments and disaster recovery use cases.

Hybrid Data Management Made Easy

The Actian Data Platform is a hybrid and multi-cloud data platform for today’s modern data management requirements. The Actian platform provides a universal data fabric for all modern computing environments. Data engineers leverage a low-code and no-code set of data integration tools to process and transform data across environments. The data platform provides a modern and highly efficient data warehouse service that scales on-demand or manually using a scheduler. Data engineers and administrators can configure idle sleep and shutdown procedures as well. This feature is critical as it greatly reduces cloud data management costs and resource consumption.

The Actian platform supports popular third-party data integration tools leveraging standard ODBC and JDBC connectivity. Data scientists and analysts are empowered to use popular third-party data science and business intelligence tool sets with standard connectivity options. It also contains best-in-class security features to support and assist with regulatory compliance. In addition to that, the data platform’s key security features include management and data plane network isolation, industry-grade encryption, including at-rest and in-flight, IP allow lists, and modern access controls. Customers can easily customize Actian Data Platform deployments based on their unique security requirements.

The Actian Data Platform components are fully managed services when run in public cloud environments and self-managed when deployed on-premises, giving you the best of both worlds. Additionally, we are bringing to market a transactional database as a service component to provide additional value across the data management spectrum for our valued customers. The result is a highly scalable and consumable, consistent data fabric for modern hybrid cloud analytics.

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How Low-Code FileOps Ensures a Seamless Digital Transformation

In an era where data stands as the driving force behind the sweeping wave of digital transformation and GenAI initiatives, FileOps is emerging as a true game-changer. Defined as a low-code/no-code methodology for performing and streamlining file operations, FileOps enables organizations to expedite their digital transformation and GenAI initiatives by empowering them to effectively manage […]

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Introducing DataConnect 12.2

We are pleased to announce the general availability of DataConnect 12.2. This release furthers our commitment to making it easy for our integration customers to connect, transform, and analyze data. With this release, customers will now have a faster path to the cloud using the Actian Data Platform to deploy existing integrations to cloud-based environments.

Highlights of the release include:

Bulk deployment of integrations to the Actian Data Platform

Previously, customers would need to deploy integrations to the Actian Data Platform individually. Customers can now take existing, or new .djars (projects/workflows) and deploy them to Integration Manager in bulk. This allows designers to expose integrations faster in cloud environments and applications.

EZScript Debugger Capability

The EZScript debugger makes designing and validating maps and processes that leverage EZScript much easier. Users can set breakpoints and test desired or expected behavior of EZScripts. Now all the debugging can be done in line with the DataConnect Studio editor. Engine execution pauses based on the breakpoints the user sets and values are displayed for any variable, macro, or object.

Integration Manager Client is now available in DataConnect Studio

This provides the ability to log into Integration Manager from the DataConnect Studio, browse existing configurations, and download the .djar and project files to the DataConnect studio if desired. Users have the option to explode the .djar files and import all the contents to a new project. This benefits clients that need to troubleshoot, edit, or extend integrations in Integration Manager.

New troubleshooting assistant

Provides detailed information about errors, suggestions on error resolution, as well as links to the Actian community posts and relevant help topics.

UX enhancements

For macros, Process Designer, message objects, and browsing. Macros can be created, edited, and accessed inline using DataConnect Studio and saved for reuse in the macro library.

Improved Data Quality metrics

Profile data to ensure data quality, including the ability to perform fuzzy matching across multiple fields in a dataset to identify duplicate records.

Direct upgrade

For customers on version 9 or later – full backward compatibility.

Click here to access the full release notes.

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How to Use Cloud Migration as an Opportunity to Modernize Data and Analytics

Ensuring a hassle-free cloud migration takes a lot of planning and working with the right vendor. While you have specific goals that you want to achieve by moving to the cloud, you can also benefit the business by thinking about how you want to expand and optimize the cloud once you’ve migrated. For example, the cloud journey can be the optimal time to modernize your data and analytics.

Organizations are turning to the cloud for a variety of reasons, such as gaining scalability, accelerating innovation, and integrating data from traditional and new sources. While there’s a lot of talk about the benefits of the cloud—and there are certainly many advantages—it’s also important to realize that challenges can occur both during and after migration.

Identify and Solve Cloud Migration Challenges

New research based on surveys of 450 business and IT leaders identified some of the common data and analytics challenges organizations face when migrating to the cloud. They include data privacy, regulatory compliance, ethical data use concerns, and the ability to scale.

One way you can solve these challenges is to deploy a modern cloud data platform that can deliver data integration, scalability, and advanced analytics capabilities. The right platform can also solve another common problem you might experience in your cloud migration—operationalizing as you add more data sources, data pipelines, and analytics use cases.

You need the ability to quickly add new data sources, build pipelines with or without using code, perform analytics at scale, and meet other business needs in a cloud or hybrid environment. A cloud data platform can deliver these capabilities, along with enabling you to easily manage, access, and use data—without ongoing IT assistance.

Use the Cloud for Real-Time Analytics

Yesterday’s analytics approaches won’t deliver the rapid insights you need for today’s advanced automation, most informed decision-making, and the ability to identify emerging trends as they happen to shape product and service offerings. That’s one reason why real-time data analytics is becoming more mainstream.

According to research conducted for Actian, common technologies operational in the cloud include data streaming and real-time analytics, data security and privacy, and data integration. Deploying these capabilities with an experienced cloud data platform vendor can help you avoid problems that other organizations routinely face, such as cloud migrations that don’t meet established objectives or not having transparency into costs, resulting in budget overruns.

Vendor assessments are also important. Companies evaluating vendors often look at the functionality and capabilities offered, the business understanding and personalization of the sales process, and IT efficiency and user experience. A vendor handling your cloud migration should help you deploy the environment that’s best for your business, such as a multi-cloud or hybrid approach, without being locked into a specific cloud service provider.

Once organizations are in the cloud, they are implementing a variety of use cases. The most popular ones, according to research for Actian, include customer 360 and customer analytics, financial risk management, and supply chain and inventory optimization. With a modern cloud data platform, you can bring almost any use case to the cloud.

Drive Transformational Insights Using a Cloud Data Platform

Moving to the cloud can help you modernize both the business and IT. As highlighted in our new eBook “The Top Data and Analytics Capabilities Every Modern Business Should Have,” your cloud migration journey is an opportunity to optimize and expand the use of data and analytics in the cloud. The Avalanche Cloud Data Platform can help. The platform makes it easy for you to connect, manage, and analyze data in the cloud. It also offers superior price performance, and you can use your preferred tools and languages to get answers from your data. Read the eBook to find out more about our research, the top challenges organizations face with cloud migrations, and how to eliminate IT bottlenecks. You’ll also find out how your peers are using cloud platforms for analytics and the best practices for smooth cloud migration.

Related resources you may find useful:

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Data Analytics for Supply Chain Managers

If you haven’t already seen Astrid Eira’s article in FinancesOnline, “14 Supply Chain Trends for 2022/2023: New Predictions To Watch Out For”, I highly recommend it for insights into current supply chain developments and challenges. Eira identifies analytics as the top technology priority in the supply chain industry, with 62% of organizations reporting limited visibility. Here are some of Eira’s trends related to supply chain analytics use cases and how the Avalanche Cloud Data Platform provides the modern foundation needed to make it easier to support complex supply chain analytics requirements.

Supply Chain Sustainability

According to Eira, companies are expected to make their supply chains more eco-friendly. This means that companies will need to leverage supplier data and transportation data, and more in real-time to enhance their environmental, social and governance (ESG) efforts. With better visibility into buildings, transportation, and production equipment, not only can businesses build a more sustainable chain, but they can also realize significant cost savings through greater efficiency.

With built-in integration, management and analytics, the Avalanche Cloud Data Platform helps companies easily aggregate and analyze massive amounts of supply chain data to gain data-driven insights for optimizing their ESG initiatives.

The Supply Chain Control Tower

Eira believes that the supply chain control tower will become more important as companies adopt Supply Chain as a Service (SCaaS) and outsource more supply chain functions. As a result, smaller in-house teams will need the assistance of a supply chain control tower to provide an end-to-end view of the supply chain. A control tower captures real-time operational data from across the supply chain to improve decision making.

The Avalanche platform helps deliver this end-to-end visibility. It can serve as a single source of truth from sourcing to delivery for all supply chain partners. Users can see and adapt to changing demand and supply scenarios across the world and resolve critical issues in real time. In addition to fast information delivery using the cloud, the Avalanche Cloud Data Platform can embed analytics within day-to-day supply chain management tools and applications to deliver data in the right context, allowing the supply chain management team to make better decisions faster.

Edge to Cloud

Eira also points out the increasing use of Internet of Things (IoT) technology in the supply chain to track shipments and deliveries, provide visibility into production and maintenance, and spot equipment problems faster. These IoT trends indicate the need for edge to cloud where data is generated at the edge, stored, processed, and analyzed in the cloud.

The Avalanche Cloud Data Platform is uniquely capable of delivering comprehensive edge to cloud capabilities in a single solution. It includes Zen, an embedded database suited to applications that run on edge devices, with zero administration and small footprint requirements. The Avalanche Cloud Data Platform transforms, orchestrates, and stores Zen data for analysis.

Artificial Intelligence

Another trend Eira discusses is the growing use of artificial intelligence (AI) for supply chain automation. For example, companies use predictive analytics to forecast demand based on historical data. This helps them adjust production, inventory levels, and improve sales and operations planning processes.

The Avalanche Cloud Data Platform is ideally suited for AI with the following capabilities:

Supports rapid machine learning model training and retraining on fresh data.
Scales to several hundred terabytes of data to analyze large data sets instead of just using data samples or subsets of data.
Allows a model and scoring data to be in the same database, reducing the time and effort that data movement would require.
Gives data scientists a wide range of tools and libraries to solve their challenges.

This discussion of supply chain sustainability, the supply chain control tower, edge to cloud, and AI just scratch the surface of what’s possible with supply chain analytics. To learn more about how the Avalanche Cloud Data Platform, contact our data analytics experts. Here’s some additional material if you would like to learn more:

· The Power of Real-time Supply Chain Analytics

· Actian for Manufacturing

· Embedded Database Use Cases

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