Author: Nir Livneh

Unlocking the Power of Cloud-Based Digital Transformation: The Importance of Data Integration

Posted on by Nir Livneh

Digital transformation has become a critical strategy shift for businesses in recent years. 

Companies are leveraging digital technologies to improve their business processes, enhance customer experiences, and increase efficiency. However, the success of digital transformation depends largely on data integration.

Data integration is the process of combining data from various sources to create a unified view. This unified view enables businesses to gain insights, make informed decisions, and automate processes. Without proper data integration, companies risk creating data silos, where each department has its own set of data that is not easily accessible to others.

Here are some reasons why data integration is crucial for digital transformation:

  • Accurate and Consistent Data

Data integration ensures that all the data in a system is accurate and consistent. This is important because inaccurate or inconsistent data can lead to poor decision-making and wasted resources. With data integration, companies can ensure that all the data they are working with is correct, up-to-date, and complete.

  • Improved Customer Experience

Digital transformation is often driven by the need to improve customer experiences. With data integration, businesses can gain a 360-degree view of their customers, which can help them understand customer needs, preferences, and behavior. This can lead to better products, services, and marketing campaigns that are tailored to the needs of the customer.

  • Streamlined Operations

Data integration can help streamline business operations by automating processes, reducing manual effort, and improving efficiency. For example, if a company integrates its sales, marketing, and customer service data, it can automate its lead generation process, improve customer service, and reduce support response times.

  • Better Business Operation Insights

Data integration enables businesses to gain better insights into their operations. By combining data from different sources, companies can identify patterns, trends, and anomalies that might not be apparent when looking at individual data sets. These insights can be used to inform business strategy, optimize operations, and drive growth.

  • Faster Time-to-Market

Digital transformation often involves creating new products, services, and business models. Data integration can help accelerate time-to-market by enabling faster decision-making and more efficient processes. By integrating data from multiple sources, businesses can quickly identify market trends, respond to customer needs, and launch new products and services.

The emergence of cloud computing has had a significant impact on data integration. Cloud-based data integration platforms offer a scalable, cost-effective, and flexible solution for businesses to integrate their data. Here are some of the benefits of cloud-based data integration:

  1. Scalability: Cloud-based data integration platforms offer unlimited scalability. This means that businesses can easily scale up or down depending on their data integration needs. As the volume of data grows, businesses can increase their storage capacity and processing power.
  1. Cost-Effective: Cloud-based data integration platforms can be more cost-effective than on-premises solutions. They eliminate the need for businesses to invest in expensive hardware, software, and maintenance. This allows businesses to focus on their core competencies while reducing their IT costs.
  1. Flexibility: Cloud-based data integration platforms offer greater flexibility. Businesses can access their data from anywhere, at any time, and from any device. This enables remote working, collaboration, and faster decision-making.
  1. Security: Cloud-based data integration platforms offer robust security features. Data is encrypted in transit and at rest, and access is controlled by user authentication and authorization. This ensures that data is secure and compliant with regulatory requirements.

In conclusion, data integration is a critical component of digital transformation. It ensures that businesses have access to accurate and consistent data, improves the customer experience, streamlines operations, provides better business insights, and enables faster time-to-market. Cloud-based data integration platforms offer a scalable, cost-effective, and flexible solution for businesses to integrate their data.

Five Common Change Data Capture (CDC) Best Practices

Posted on by Nir Livneh

Why capturing changes to your data in real-time is so vital to your business

The ability to respond rapidly to customer behavior and market changes is transforming the landscape of “business as usual.” From industrial process optimization in the manufacturing industry to fraud detection in finance and ad personalization in retail, companies across industries are increasingly looking to identify and take action on opportunities in their data in real-time.

But supplying business leaders with insights for real-time decision-making is no easy feat. In most enterprises, key operational data is spread across hundreds of different systems, so a common practice is to extract and move all relevant data to a central location for analysis (for example, an enterprise data warehouse/data lake/enterprise data hub). But since most operational systems write all their data to a relational database, one of the main challenges to enabling real-time analysis is – how to capture changes from the relational databases in real-time?

This is where CDC comes into play. No, I am not talking about the Centers for Disease Control and Prevention (CDC) in the US … CDC stands for Change Data Capture, which means a set of best practices to capture the data changes that an app issues.

In this post, I’ll focus on relational database CDC, though the term also applies to other repositories, such as NoSQL databases, storage systems, cloud services, etc. The goal is to survey and score the range of CDC options available – and to guide technology leaders in thinking through which approaches are best-suited to their business’ needs.

Five common CDC best practices:

1. Dual writes in the application

  • How it works: You can go ahead and change the application itself to write each change both to its database and to a log file or a message queue, so you’ll have the list of changes ready somewhere.
  • Advantages: Few; in practice, it is rarely desirable or even possible, for reasons outlined below.
  • Disadvantages: To start with, in many cases you do not have access to the application source code (for example, for packaged applications), or a good understanding of all its write paths, or an ability to test such changes thoroughly. Also, that approach is labor-intensive – you will likely need to start from scratch for each application. And the final straw – it is very hard to make both writes (to the application database and the custom logs) atomic – that either both succeed or that both fail.

2. Network sniffing

  • How it works: Some tools provide a way to capture the database inbound traffic and reverse engineer the application requests.
  • Advantages: Low overhead.
  • Disadvantages: Generally not used due to a couple of critical flaws. First, it does not capture changes from bulk operations (a single statement that changes a lot of rows based on a query) or from calling a stored procedure. Second, whenever the network sniffing is down, all uncaptured changes will be lost.

3. Database triggers

  • How it works: A database trigger is an optional piece of user code, that the database could be configured to run as part of any change to the rows of a table. A trigger could be used to log any change to a side table, so the list of changes could be queried from it later.
  • Advantages: Don’t require application changes and can capture all standard changes.
  • Disadvantages:
    • They run as part of the operational transaction, slowing it down. Even worse, it makes them disruptive – if they run into an unexpected error and throw an exception, the user transaction will fail, breaking the operational system.
    • Using a database table to track changes consumes database storage, and requires an additional step to remove old data periodically.
    • Triggers tend to become disabled or invalid over time, for many reasons ranging from table schema changes to various admin operations. Any change that happens until they are fixed will be lost.

4. Periodic queries

  • How it works: This group of techniques involves running a periodic SQL query to identify some types of changes. It relies on having some table property to identify changes efficiently – for example, a “last updated” timestamp column or an ascending integer primary column, etc.
  • Advantages: Periodic queries have low overhead if the source table is properly indexed, and while they miss intermediate changes, they do catch up nicely after an outage and can support schema changes.
  • Disadvantages: Depending on the specific technique, it will likely only identify some types of changes (for example, only INSERT or only INSERT/UPDATE), and will always provide just a delta between the periodic queries, so it will miss some intermediate change states in a sequence of changes.

5. Transaction log processing

  • How it works: Relational databases write any change to their data to a transaction log – an internal mechanism that allows them to correctly recover from failure or to be restored to any point in time, if needed. The transaction log could be parsed (sometimes with the help of a built-in database infrastructure) to extract the changes stored within them.
  • Advantages: This method allows capturing all changes of all types in an asynchronous fashion. It does not require changes in the database schema or the application.
  • Disadvantages: While it typically has minimal overhead, It is, however, harder to implement.

Be Ready for Performance and Application Impact Challenges As You Scale

As we saw, there are many possible ways directions to implement a database CDC solution. While each might have its niche, our experience is that transaction log processing is generally the most powerful solution, leading to the lowest latency and overhead while being able to capture every single change.

However, picking a CDC best practice is only the starting point. In order to achieve a performant and reliable solution, there are many other considerations. For example – how to correctly handle disconnects and processing errors? How to make sure no change is lost or duplicated in all failure scenarios (exactly-once guarantees)? How to sync the initial data capture and the starting time of the CDC? How to minimize the overhead on the source database? How to minimize CDC latency and maximize its throughput?

Equalum’s data ingestion technology is purpose-built to harness the power of open-source frameworks Spark and Kafka in an end-to-end solution. And we built the most robust and powerful CDC approach on the market to address the performance and application impact challenges companies face as they scale.

When and Why Real-Time Data Matters

Posted on by Nir Livneh

Why Real-Time Data Matters for Your Business

Attendees at any big data or data science conference might very well leave believing that the future will be dominated entirely by the use of real-time data. Batch is dead. Long live real-time!

There’s certainly no shortage of heady optimism about the future of real-time data. But proponents haven’t always been rigorous in defining why and in what situations real-time data matters. As a result, some technology leaders have rightfully pushed back, questioning whether batch data and legacy ETL processes are “good enough.”

Real-Time Data can Improve Business Analytics and Operations

The reality is that the use of batch data for analysis and decision-making isn’t going away any time soon – because there is still a place for it. Architecting a streaming data solution in order to report on last month’s financial numbers would be unnecessary.

That being said, real-time data is a must for any application where the cost of data latency is high. Businesses think of the cost in many ways, but generally, it falls into the bucket of lost revenue (e.g., from customer churn or inventory shortages) or actual financial outlays (e.g., for equipment repair or security remediation).

Successful Customer Facing Experiences Often Require Real-Time Response

Here are a few situations where real-time data is critical – and what industry leaders are doing to take advantage of the opportunity afforded by real-time data technologies:

  • Customer workflows: Customer expectations for immediacy and personalization are rapidly changing, and businesses from retail to financial services are struggling to keep pace. The cost of data latency in customer-facing experiences can have serious consequences: irrelevance (leading to erosion in brand perception/loyalty) or friction in the customer journey (leading to drop-off and lower conversion). For example, a retailer serving a display advertisement for a product that a customer just purchased creates an alienating user experience. Similarly, a customer seeking an auto loan is likely to favor the bank with an instant loan review and approval process over the one that takes minutes or even hours.
    • Example: A leading media company correlates viewership and social media data to inform ad buying decisions in real-time – investing in the content and ad platforms that are most relevant to their viewing audiences.

Use Real-Time Data to Prevent Cost Escalations for Your Business

  • Cost containment: Real-time data can afford critical insights for preventing cost escalation. For example, real-time supply chain optimization can help companies predict and remediate critical inventory issues before shortages result in missed sales or require costly interventions. Similarly, industrial manufacturers may rely on real-time analysis of machine data to optimize preventative maintenance, preventing equipment damage that can be devastating to manufacturing output.
  • Health providers interpreting results from network-connected devices can detect anomalies in real-time, preventing patient health emergencies like strokes or heart attacks.
    • Example: A Fortune 100 industrial manufacturing company makes use of a digital twin to identify anomalies in the and optimize preventative maintenance on equipment.

Respond Quickly to Cybersecurity Threats using Real-Time Data to Detect Anomalies

  • Threat detection: The growth of cybersecurity threats has placed an increased premium on threat detection (including network, application, endpoint, cloud, and wireless security). While security breaches represent can result in staggering direct and indirect costs to businesses, response speed has a significant impact on the ultimate cost of the breach.
    • Example: leading financial institutions monitor network traffic in real-time in order to detect anomalies that could signal intrusion attempts.

Ultimately, real-time data can provide a critical edge that helps enterprises navigate today’s fast-paced business landscape.

5 Insider Tips Before You Embark On A Streaming Data Architecture

Posted on by Nir Livneh

With the emphasis on moving to a Streaming Data Architecture, it might feel like a rip and strip of your current architecture is the only way to ensure success, but that’s hardly the case. Trying to replace complex, existing systems is not the first move, much less is it something that your team can culturally adopt. Bottom line, change is hard, so smaller steps can often lead to quicker adoption and long term gains.

#1) Consider a Greenfield Project with new revenue streams to Implement Streaming & Build Cultural Adoption

So often cultural adoption is what keeps organizations from embarking on a streaming journey. The Data Giants (or sometimes dinosaurs) are reticent to break from what is known and functional. At its core, new technology could be a threat to their understanding and even job security. Proceed knowing that as your Architecture becomes streamlined, and the lean on the tech team lightens, there will be new room for innovation, collaboration and future projects where experience and enthusiasm from all players can be fully utilized.

Take a good look at the business and identify where you see bottlenecks that, with real-time data instead of batch, could be a quick win and even generate new revenue streams. Give your team and business leaders a chance to see true value in what streaming can achieve. Once you demonstrate success and the viability of the new revenue channel, this same architecture can be brought to other areas of your organization more quickly. You’ll not only have proof of concept, but infrastructure in place and buy in from those who will need to learn and work with your new system.

#2) If you’re choosing DIY approach, prepare for a long haul

For years, many companies had ETL tools in place and are now transitioning to open source technologies like Kafka, Spark or Hadoop as they try to embrace streaming on a larger scale. But as people who have walked the DIY path know all too well, this tech can be hard to maintain, manage and to build on. Sure, open source frameworks are free, but come loaded with required tech expertise, coding and a lack of support when things go wrong. You will invest countless hours patching your system in the gray area between platforms, and lose valuable ground when a team member leaves, taking coding and expertise along with them. Before you know it, you will be trying to maintain and manage an unwieldy monster of a system – recreating the wheel when you don’t have to. It might work, but you will spend more time on that than anything else in IT.

#3) Change Data Capture is KEY

Ultimately, we all want to avoid risk as much as possible. If you want to stream data from data sources, querying a billion records every second is not going to work and neither will rewriting the application. Change Data Capture is your bridge – capturing data changes at the source. CDC will capture incremental changes on the data by listening to change mechanisms off the source – i.e. database transaction logs, journaling mechanisms within messaging services, rest APIs, etc. Each source is very unique and very different. CDC is how you will solve the first and hardest problems of a streaming architecture – capturing data from the source in a seamless way without making any changes to the application.

#4 – Use Replication Groups to Simplify Replication of High Volumes of Data and Changes

Replication Groups allow a streaming-first data platform to process non-stop changes to groups of tables in one shot. If multiple tables are updated in one transaction, that would be captured altogether. By using replication groups, you can select tables for replication by name or name patterns. If replication groups are not supported, then the user would be forced to create data flows for each table with independent flow execution on each – a laborious process and error prone to say the least.

Schema Evolution is a common feature of replication groups, providing full support for database schema evolution (schema changes) in an automated manner with options for the customer to determine how he/she wants to propagate schema changes. When a new table appears at the source and its name matches the replication pattern, it will create a table in the target database and start replicating it immediately. When considering streaming-first data platforms, replication groups and schema evolution should radically simplify massive data streaming processes.

#5) Find a Data Ingestion Solution that simplifies Your Data Architecture

Everyone talks about simplifying their architecture, but achieving a truly streamlined approach is another story. Here are few key components that you should look for:

  • An end to end platform that can accommodate Streaming Replication (for ELT), Streaming ETL and Batch ETL – ideally with a no coding, drag and drop UI
  • Easy to deploy, easy to onboard and with ongoing support from start to finish
  • A fully orchestrated platform that provides ease of monitoring, alerting and management
  • Scalable – grows with you as your data volume, processing complexity and use cases grow too
  • Cloud-agnostic, multi-cloud or on-premises
  • Transformation, Aggregation and Correlation
  • On cutting edge, best-of-breed distributed processing open-source frameworks such as Spark and Kafka
  • Exactly once guarantee
  • Ensure high availability and enterprise grade security
  • Failure recovery from sources and targets.

Streaming data will change the way that your business operates. It’s an exciting new chapter in operating, but one that needs a thoughtful and strategic approach for implementation. When the right steps are put into place from the start, you and your organization can reap the many benefits to follow.

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