Have you ever noticed the speed at which your favorite online service adapts to your preferences, offering tailored recommendations and real-time updates? Such adaptability is not just a user-friendly feature; it’s a direct result of the capabilities of real-time data processing. In today’s age, marked by rapid data exchange, swiftly analyzing and responding to information has become a fundamental aspect of modern operations. Snowflake, a cloud-based platform, has revolutionized the data landscape with its distinctive architecture and seamless scalability. In this era, where data is the new currency, the ability to ingest data in real time becomes crucial. Snowpipe streaming, a feature of Snowflake, addresses this need, ensuring that data, as soon as it arrives, is immediately available for querying and analysis. This capability not only bolsters the efficiency of data-driven decisions but also ensures that businesses can act on fresh insights without delay.

This blog offers an overview of Snowpipe streaming and dives into essential aspects of Snowflake, such as real-time data ingestion, replication strategies, and optimizing Snowpipe for peak performance. Furthermore, it addresses how Snowflake empowers businesses to efficiently analyze and act upon fresh insights, offering a comprehensive understanding of its transformative capabilities for businesses.

Snowpipe’s streaming framework

To fully understand Snowpipe’s near real-time data ingestion capabilities, let’s explore its innovative architectural framework and seamless integration with Snowflake’s cloud platform.

Snowpipe’s serverless architecture is the key feature of its highly efficient data ingestion process. This architecture eliminates the need for manual server management, simplifying the data pipeline. Users no longer have to worry about provisioning, maintaining, and scaling server instances. As a result, this approach is not only streamlined but also cost-effective, as it operates on a pay-as-you-go model. This ensures optimal resource allocation and consistent performance. Snowpipe’s serverless design takes advantage of event-driven processing, promptly responding to data source events. It automatically allocates and scales resources to handle various data workloads. This architectural choice empowers businesses to effortlessly process streaming data, enabling them to make informed, data-driven decisions and gain innovative insights through near real-time analytics.

Moreover, Snowpipe seamlessly integrates with Snowflake’s cloud-native platform, leveraging the latter’s data warehouse capabilities. This integration ensures that data ingested through Snowpipe is seamlessly integrated with Snowflake’s power and efficiency.

Real-time data ingestion

In today’s data-centric landscape, achieving a seamless data flow is no longer a luxury but a strategic imperative for businesses to thrive and evolve. To process information effectively from various sources, it’s important to comprehend the mechanism behind this process.

To ace data streaming—Snowpipe utilizes continuous data polling. Cloud storage repositories are keenly observed, with systems constantly monitoring for new data arrivals. As soon as data files turn up, they’re immediately fetched and funneled to the processing pipeline. This approach ensures all data is deeply checked and processed.

Immediate data ingestion prevails because of its compatibility with a multitude of data file formats. Whether the data is structured in forms like CSV or semi-structured data like JSON and XML, or binary formats like Avro, near real-time data processing supports them all. But how does Snowpipe make sense of diverse data? It’s through its Parsing mechanisms. The mechanisms prevail in dissecting the data of all the incoming data files, extracting relevant information, and organizing it for additional processing. Its process encompasses decoding binary formats, validating data against defined schemas, and enhancing data into a standardized format compatible with analysis.

Let’s consider, near real-time data ingestion operates as a high-speed highway, in which continuous data polling functions as the fast-moving traffic and parsing mechanisms play the role of smart toll booths along the route. This effective mechanism ensures that businesses can rapidly process and analyze the data as it flows, similar to vehicles passing through toll booths without slowing down. This optimized process empowers organizations to maintain a smooth, undisturbed journey toward their data-driven goals.

Understanding replication strategies

As we further explore Snowpipe’s capabilities, the other feature that shines in Snowflake—is Database Replication. These features enable near real-time data synchronization between databases, ensuring that updates and changes are automatically reflected on another database, thereby maintaining consistency and accuracy across their entire database structures. These mechanisms are instrumental in maintaining data reliability and accessibility.

The role of Continuous Data Protection (CDP)

Data replication strategies play a crucial role in maintaining healthy data integrity and resilience within the architecture. Continuous Data Protection (CDP) is at the heart of these replication strategies. CDP protects data against unexpected disruptions and breaches by regularly recording changes made to data, either from user interactions or from external data ingestion processes like streaming. These changes are precisely logged, creating an immediate data trail that can be conducive in scenarios like auditing and data recovery.

Time-travel ability

The other remarkable aspect of Snowflake’s data replication strategy is its Time-Travel ability. This replication feature enables users to access previously stored versions of the data, effectively retrieving data at any point in the data’s history. This not only aids in forensic analysis but also helps compare data states for making corrections when needed.

Failover mechanism

Finally, the failover mechanisms serve as a backup, ensuring that data processing remains uninterrupted. During the event of disruption or outrage, it automatically redirects data traffic to a backup cluster, maintaining downtime and assuring high availability. Replicating strategies like CDP, time travel, and failover helps businesses make informed decisions about data management, resource allocation, and disaster recovery.

Integration point: IoT devices and event sources

Integrating IoT devices and event sources is pivotal in data-driven environments. These integration points offer the means to connect and collect data from IoT devices, including machines, sensors, and other smart devices. Additionally, they integrate with event sources like Apache Kafka, enabling organizations to automate data collection, access near real-time insights, and enhance operational efficiency and the user experience.

Connectors and SDKs: Snowpipe provides an array of connectors and Software Development Kits (SDKs) designed to ease the process of integration. These connectors and SDKs function as a bridge between IoT devices, event sources, and the user’s Snowflake data platform. They streamline the process of transferring data from these sources into Snowflake, irrespective of the device or system the user employs.

Handling data streams: Snowpipe is meticulously crafted to handle data streams. It seamlessly handles data streams from event sources like Apache Kafka through an optimized process. Snowpipe constantly monitors the Kafka stream, staying alert for new data events. As soon as the data is detected, it automatically triggers the ingestion process, immediately fetching the new data events and directing them to Snowflake’s data processing pipeline without manual intervention. Due to its adaptable architecture, Snowpipe can concurrently handle data from various Kafka topics, ensuring prompt data ingestion during peak times. In the aftermath of ingestion, Snowpipe prepares the data for immediate analysis, empowering businesses to make decisions based on the most recent data streams.

Use case

Consider the application of Snowflake’s Snowpipe streaming in the healthcare domain. Wearable IoT devices, such as heart monitors and glucose meters, consistently produce crucial patient data. By leveraging Snowflake’s Snowpipe streaming, hospitals can access near real-time data, facilitating immediate alerts and prompt medical interventions. Snowflake’s capability to transform data into insights allows hospitals to discern health patterns, paving the way for more effective care. Additionally, Snowpipe’s encrypted data transmission safeguards the security of the medical data. This monitoring system, which uses Snowflake as its power source, improves patient care by encouraging a more connected patient experience.

Optimizing Snowpipe for peak performance  

Now that we have addressed the capabilities and functionality of Snowpipe, it’s also vital to understand how to harness and optimize it for peak performance. The following are a few strategies to ensure Snowpipe operates efficiently, minimizing latency and maximizing data throughput.

• Batch data: Snowpipe has the ability to process large volumes of data. Therefore, instead of ingesting data in small chunks, opt to batch them. This is conducive to Snowpipe as it reduces the number of calls, resulting in efficient data processing and reduced costs.
• Data compression: To speed up the processing, compress the data before ingesting. As Snowpipe supports various compression algorithms, choose the one that best suits your data size and type.
• Frequent maintenance: It’s a healthy practice to regularly review and update your Snowpipe configuration. As your data grows and changes, your configurations might need tweaks and adjustments to maintain peak performance.
• Network optimization: Always maintain a robust network connection between the data source and Snowflake. Network issues can substantially slow down the ingestion of data.

Unlock the power of Snowflake with Indium Software

Indium Software offers a holistic, one-stop solution that addresses all your data needs, delivering uninterrupted support and guidance throughout the entire data lifecycle. Their services include data warehousing, Snowflake implementation, migration, integration, and analytics. Going beyond mere Snowflake support, Indium Software ensures a seamless and effective experience with the platform, excelling in providing robust and governed access to your data.

The company facilitates seamless access across cloud environments and offers expert assistance for secure data sharing. With Indium Software’s profound Snowflake integration and implementation expertise, businesses can fully unlock their data’s potential, ushering in a transformative, data-driven future.

Conclusion

Snowpipe streaming is a remarkable feature within Snowflake’s ecosystem, redefining the way businesses handle and process data ingestion in near real time. By leveraging Snowpipe, organizations can swiftly access data-driven insights, enabling faster and more informed decision-making. Snowpipe empowers businesses to stay agile and competitive by responding timely to user preferences, ensuring data integrity, and bolstering availability. With Snowpipe Streaming, the future of data is within reach. Connect with experts at Indium Software today to harness the power of near real-time data.

The post Snowpipe Streaming: Real-time Data Ingestion and Replication Strategies appeared first on Indium.

This will help to effect continuous improvement in performance and create differentiated experiences for customers and employees. It will also enable accelerated development of innovative new solutions.

McKinsey also identifies the current challenges to optimizing data sources as

• Limited capabilities of legacy technologies
• Challenges in modernizing the architecture.
• Demand for high computational resources for real-time processing jobs

This results in only a small part of the data from connected devices being leveraged. As companies balance speed and computational intensity, they are unable to perform complex analyses or implement real-time use cases.

Getting the right data technologies to ingest, process, analyze, and visualize in real-time is going to be a game-changer in improving decision-making, enhancing customer experience, and accelerating growth.

Improved Decision Making

Real-time data is critical for conducting real-time analytics, which helps with faster decision-making. Data is collected from a variety of sources, including sensors, databases, operational systems, cameras, and social media feeds with minimal delay and processed and analyzed quickly. They could be alerts and notifications or inputs from user behavior.

Real-time data can be of two types:

• Event Data: The generation of a collection of data points based on well-defined conditions within a system.
• Stream Data: The continuous generation of a large volume of data without any identifiable beginning or end.

Easy access to data in real-time data enables a quick drawing of insights to make informed decisions and be responsive as events unfold. It helps with capturing trends, both past, and present, and can be analyzed in real-time to decide on the next course of action.

Some of the benefits of real-time data include

Being Proactive

In the absence of real-time data, there is a lag between insights and responses. This reactive approach can prove costly, resulting in losing customers or production-related issues escalating. Real-time data analytics allows enterprises to proactively approach developments and respond appropriately.

Enhance Customer Experience

Visibility and transparency have become key in several client-business relationships. It helps improve decision-making based on project status and enhances customer experience and retention. Responding to customer requirements and empowering them with information in real-time further strengthens the relationship between the two.

To know more about how Indium can help you, please check out more.

Unify Data

Different teams end up creating data silos to suit their requirements. This can distort the view when making strategic decisions at the enterprise level and delay the process. A cloud-based data streaming solution helps to provide a unified view in real-time while allowing different teams access to secure and permission-based data they need to make decisions for their department.

Improve Operational Excellence

Real-time data allows you to manage your organization’s assets proactively. It lets you plan downtimes for maintenance and repair, improves the life of the assets, and take timely steps to replace, where needed, with minimum disruption to operations. This naturally leads to a better quality of products and services and improved profit margins as it lowers overheads.

Striim Power For Real-time Data Analytics

The Striim unified real-time data integration and streaming platform unifies data across multiple sources and targets. It offers built-in adapters and supports more than 125 sources and targets, enabling the management of multiple data pipelines in a Striim cluster. Striim 4.1 offers features such as OJet to let customer applications read multi-terabytes of data per day and a high-performance Oracle Change Data Capture (CDC) reader. It also sends real-time alerts and notifications to identify emerging workload patterns and facilitates collaboration between developers and database administrators.

Striim users can build smart real-time data pipelines quickly for streaming large volumes of events daily. It is scalable and secure, and the features are highly available. It is easy to maintain and allows the rapid adoption of new cloud models, infrastructure modernization, and digitalizing legacy systems.

Striim enables data integration using a streaming-first approach, supporting incremental, real-time views in the cloud database and the streaming layer. It includes Streaming SQL to facilitate real-time analytics of data, as also train machine learning models in real-time.

Business analysts, data scientists, and data engineers can use Streaming SQL to build data pipelines quickly and without the need for custom coding. Striim also allows data movement in real-time, because of which stream processing applications need to operate continuously for years. These further speeds up decision-making as insights can be drawn quickly, without latency between receiving the data and running analytics on it.

Check out our case study on real-time data analytics

Case and Point: Simplifying Healthcare Predictions in 3 Expert Steps

Understanding Symptom Patterns: Our first step involves data acquisition and thorough analysis of historical patient data. We tap into the treasure trove of symptoms, medical records, and outcomes to discern intricate patterns that might remain hidden from traditional analysis.

Feature Engineering with Domain Knowledge: With a team of domain experts, we transform raw symptom data into meaningful features. These features are carefully curated to capture the nuances of various symptoms, their interplay, and potential implications. Our domain knowledge empowers us to create a robust feature set that forms the foundation of accurate predictions.

Advanced Machine Learning Models: Equipped with a rich feature set, we employ advanced machine learning models. From ensemble methods to deep learning architectures, we evaluate and fine-tune models that can effectively map symptoms to probable outcomes. This step requires rigorous experimentation to ensure optimal model performance. 

The utilization of Symptom Pattern Analysis, Feature Engineering, and Advanced Machine Learning Models in the healthcare domain, along with Indium’s implementation of Striim for real-time data migration and processing, brings substantial and quantifiable business value to the table.

Healthcare Providers: Reduced diagnosis time through rapid predictions – from days to hours, thereby accelerating patient care. Enhanced efficiency with streamlined operations leads to quicker decisions and resource allocation. Improved patient care is achieved through early intervention based on predictions, resulting in improved treatment outcomes. Informed resource allocation provides predictive insights that optimize staff schedules, room usage, and equipment availability. Optimized treatment plans driven by personalized treatments yield better outcomes and patient satisfaction. The Cost savings achieved through fewer hospital stays, reduced redundant tests, and efficient resource use contribute to lowering costs. This not only benefits the patients but also benefits the providers by optimizing their resources.

Healthcare Payers and Insurance Companies: The implementation offers a competitive edge for healthcare providers, attracting patients and enhancing the providers’ reputation due to quick and accurate diagnoses. This, in turn, leads to efficient resource utilization, potentially reducing the overall cost of treatments. Cost savings arising from reduced hospital stays and redundant tests contribute to lower healthcare expenditures, benefiting healthcare payers and insurance companies. Healthcare payers such as insurance companies can also reduce fraudulent claims as they will have access to patient diagnosis history in real-time.

Medical Researchers and Innovators: The curated data fosters research opportunities, facilitating medical insights and potential innovation generation. The advanced analytical capabilities of Symptom Pattern Analysis and Machine Learning Models open avenues for new discoveries and improvements in medical practices, benefiting the broader healthcare research community.

Overall, the integration of advanced technologies, real-time data processing, and predictive analytics in the healthcare domain offers benefits that extend to healthcare providers, payers, patients, and the research community. This synergy drives efficiency, quality of care, and cost-effectiveness, ultimately transforming healthcare delivery and outcomes.

Indium for Instant Decisions with Striim

Indium Software, a cutting-edge solution provider, has deep expertise in Striim implementation and can help businesses create exciting digital experiences for their customers.

A private sector bank offering specialized services to 9 million customers across various business verticals and with a presence global presence required data to be updated in real-time from its core banking systems to a reliable destination database for downstream analytics. By migrating the data from legacy systems to Striim in real time, Indium helped the customer improve its responsiveness and operational efficiency apart from other benefits.

Indium’s team of Striim experts have cross-domain experience and can provide custom-built solutions to meet the unique needs of our customers.

FAQs

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The timely loading of real-time data from your on-site or cloud-based mission-critical operational systems to your cloud-based analytical systems is assured by a continuous streaming ETL solution. The data loaded for making crucial operational decisions should be reliable due to continuous data flow. By supplying an efficient, end-to-end data integration between the source and the target systems, Striim can guarantee the dependability of the stream ETL solutions. To ensure data reliability and send it to the target systems, the data from the source can be transformed in the real-time data pipeline. Striim applications can be created for a variety of use cases.

About the customer

A power company called Glitre Energi manages the power grid, retails electricity, and offers broadband services. About 90,000 people receive electricity from Glitre Energi. The organization oversees the power lines that pass through heavily populated areas.

Problems with the current design

• Metering data should be loaded to the SQL databases from event-based sources.
• Regardless of any additional parameters in the source events, metering events with the same filename should have the same ID assigned to them.
• Relational database systems have trouble normalizing real-time metering events. Unless all of the previous events have been sent to the target, comparing data in real-time and assigning values becomes difficult.

Solution Provided

• Meter values for the power supply are sent as JSON files from the source applications, which are referred to as meter events, to Azure Event Hubs.
• Due to reporting lags, each file contains n number of events with various timestamps.
• Each event must maintain the link to the file in which it was received in order to maintain traceability back to the source.
• These events are sent to two SQL server tables, one of which contains information about metering and the other of which contains information about metering files.

Also Read: Use Cases for a Unified Data Integration and Streaming Platform like Striim

Usage Of Constituents

Cache

Getting the most unique identifier from the target table is made possible by a memory-based cache of non-real-time historical or reference data that was obtained from an external source.

External Cache The need for data prompts Striim to query an external database. In order to determine whether the incoming data is present in the target table already or not, Striim queries the same data when it joins with real-time data.

Windows

By limiting the data set by a specific number of events, period, or both, Windows will aggregate, join, or perform calculations on the data. This aids in bringing the target database data and real-time data together in one location where the downstream pipeline can carry out the transformations.

Continuous Query

A continuous query that can be used to filter, aggregate, join, enrich, and transform the events specifies the logic of an application. A query facilitates the logic in the data pipeline and the combining of data from various sources.

The use case’s high-level representation is shown in the image below:

Flow Insights

• Striim application must identify the event with new files, get the filename, assign a unique integer Id, and store these values in a separate table in the SQL server database.
• For each event that is processed, the application queries an external cache to see if the filename already exists in the target table.
  • If it exists, the CQ retrieves the Id for that filename, replaces the id value with the incoming event data, and sends it to the target table.
  • If it doesn’t exist, the CQ will increment the id and assign the id to the new filename and send the data to both the target tables.
• Striim cache can be used to load the last received filenames and IDs so that the ID can be incremented.
• Striim cache should be updated regularly depending on what frequency each event has been sent to the target tables, but it effectively needs to be mutable.
• Striim windows help bound the real-time event data and the file data, so the continuous queries can use these data and make decisions accordingly.

Conclusion

Using continuous query components has made it simple to compare event data in real-time load to reach decisions. With the aid of windows and cache components, efficient data that must be retrieved from external sources has been planned out very well. The beauty of Striim allows data to be joined wherever it is needed and the desired output to be obtained, assisting Glitre Energi in achieving the normalization of their metering events in their relational systems.

Please get in touch with us if you still need help or if your needs are still unclear. Our team of professionals will always be available to assist you. Click to do so.

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Problem Statement 

As we all know, going through each stage to board a flight at an airport can be stressful. As shown in the image, there are typically five steps in the processing process: obtaining a boarding pass, bagging, security inspection, immigration inspection, and finally boarding. To prevent boarding or flight delays, all five of these processes should be finished within a SLA established for each phase. Internally, airport authorities use the message queueing system to populate each stage of the event, but the challenge would be to spot SLA breaches in real-time and report them to address the delay.

Process of Boarding at the Airport

The procedure would be monitored using MQ systems, where each step emits an event with the flight and passenger information. Finding the stage that exceeds the SLA required to pass a certain stage of checking is the challenge here. If any of the processes were to miss the SLA cut-off, the following effects would result.

1. Delaying the current flight and any subsequent flights
2. Panic among the passengers
3. Longer wait times.
4. A process could be missing.
5. There would be a security breach as a result.

The illustration below shows how MQ events are produced as passengers go through the airline’s onboarding procedure.

How does Striim contribute to process improvement?

Striim is a real-time replication tool that enables data streaming from a variety of source systems and aids in event migration to the target systems. Windowing technique is one of its key features, and it can hold data based on options like time, count, and fields to process on-the-fly. The best course of action in this situation is to hold/cache the event until the SAL/cut-off for the boarding process is determined. We can review the events that have occurred after the deadline to force the airport authorities to act right away from the cache. Assumedly, the events that are generated at each stage include the passenger’s boarding and flight information so that the specific passenger can be tracked throughout the boarding process.

Also Read: Use Cases for a Unified Data Integration and Streaming Platform like Striim

Striim Windowing Techniques

Real-time data is constrained within a window by time (for instance, five minutes), event count (for instance, 10,000 events), or both. The creation of a window is necessary for a replication flow to aggregate or process data, fill the dashboard, or send alerts when conditions deviate from expected ranges. An application can only evaluate and respond to individual events without a window to bind the data.

The three types of windows that Striim supports are sliding, jumping, and session windows. When a query’s contents change (sliding), expire (jumping), or there has been a lull in use activity, Windows sends data to the queries that follow (session). Jumping windows, which are regularly updated with an entirely new set of events, are the best fit for our use case out of these three types. Data sets for the hours of 8:00 am–8:04:59 am, 8:05 am–8:09 am, and so on would be produced, for instance, by a five-minute jumping window. A new data set would be produced by a 10,000-event jumping window after each 10,000 events. The window would output a new data set each time it accumulated 10,000 events or five minutes had passed since the previous data set was output if both five minutes and 10,000 events were specified. With the help of this Windows feature, we are putting forth an architecture that will both capture events coming from MQ systems and those that are approaching the cut-off time.

Proposed Architecture for the Airline On-Boarding System

With this suggested architecture, the airline onboarding processing can detect an SLA breach during passenger check-in for a flight with speed and accuracy. By storing the data in a designated window, it operates using the caching technique. The Striim partitioning feature enables us to classify every passenger according to their boarding pass number, allowing us to identify anyone having trouble during the flight. Striim’ s SQL-like queries are used to group and aggregate the events from jumping windows for each stage, from checking in to boarding flights.

CREATE OR REPLACE JUMPING WINDOW Boarding_Data_Window OVER admin.Boarding_Data_Win KEEP 2 ROWS WITHIN 90 SECOND PARTITION BY BoardingPassNo;

CREATE OR REPLACE CQ Passenger_Data_Boarding INSERT INTO admin.NotBoarded SELECT p.flightNo as flightNo, p.boardingPassNo as boardingPassNo , case when b.boardingPassNo is null then “Not Boarded” else “Boarded” END as BoardingStatus, b.boardingPassNo as bagBoardingPassNo FROM Boarding_Data_Window b right join PassengerDataWindow p on p.boardingPassNo = b.boardingPassNo;

CREATE OR REPLACE CQ NotBoarded INSERT INTO admin.NotBoardedResult SELECT * FROM NotBoarded n where BoardingStatus=”Not Boarded” group by n.boardingPassNo having count(*) <2 ;

Here are some benefits of using Striim as a replication tool in this scenario to record and modify SLA beaches during the flight boarding:

1. Real-time data collection that aids in processing the event at every stage.

2. Windowing the events until the designated interval to process and modify.

3. The dashboard and alerting system provides a nearly real-time progress of each passenger’s stages.

4. Quick fixes considerably shorter airport wait times and delays.

5. More accurate reporting.

An elaborate use case for Striim services: Striim-Powered Real-Time Data Integration of Core Banking System with Azure Synapse Analytics

Conclusion

The lengthy part of flying is waiting in line for the boarding process due to the densely populated airport. A more effective tracking system offers a practical way to track individual passengers for a comfortable journey. Using Striim’s windows technique, we can process and change airport authorities at any stage of the boarding process by holding every individual passenger detail in-memory directly from the real-time queuing system. Additionally, Striim aids in the migration of events to alternative target systems for better visual representations.

The Striim experts at Indium have cross-domain experience and can create solutions specifically for each of our clients’ individual needs.

The post Spot Realtime SLA Breaches in Airline On-Boarding Process Using Striim appeared first on Indium.

Gartner defines SDI (stream data integration) as a data pipeline that allows ingesting, filtering, transforming, enriching, and storing the data in a target database or a file for running analytics later. In SDI systems, event records are not a static snapshot of data at rest but, rather, a continuous, unbounded sequence of data in motion.

During data integration, event data is ingested from across the enterprise and made accessible to business users to improve decision-making in real-time for enhancing:

● Customer experience

● Minimizing fraud

● Optimizing operations and resource utilization

4 Use Cases of Data Integration Platform

Forward-looking enterprises will find streaming data integration useful for:

● Data modernization

● Real-time insights

● Operational analytics

● Digital customer touchpoints

Use Case #1: Data Modernization with Cloud Adoption

One of the first steps to modernizing operations and data & analytics solutions is cloud adoption. It begins with the migration of the on-prem database to the cloud and must be performed without disruption to the business. Streaming data integration in Striim enables this through the Change Data Capture (CDC) feature. All new transactions are captured as they happen, without pausing operations, and loaded to the cloud database once the on-prem database is loaded and ready.

Not only at the time of migration, but this feature facilitates even the bi-directional movement of data or cloud to cloud integration without interruption.

Use Case #2: Real-Time Insights

A wizard-based UI and SQL-based language in Striim allow users to develop real-time applications quickly and easily using stream data integration pipelines. Visualization and analytics on the data can be performed while it is in motion, even before the data is delivered to the target, using Striim’s SQL-based streaming analytics.

Use Case #3: Operational Analytics

Striim’s stream data integration capabilities allow users to derive operational intelligence by leveraging data from a variety of sources in real-time. The pre-processed in-flight data is delivered in a consumable format, accelerating downstream applications and providing insights into operations. Smart data architecture is made possible by stream data integration, with only necessary data that serve the end-user purpose being stored in a consumable form.

An elaborate use case of striim services: Striim-Powered Real-Time Data Integration of Core Banking System with Azure Synapse Analytics

For businesses with hybrid cloud architecture, streaming data integration connects the cloud database to enterprise-wide systems and makes it a natural part of the data center. It facilitates continuous real-time data movement from databases, log files, machine data, and other cloud sources, sensors, and messaging systems to transform cloud workloads into operational workloads.

Striim also helps to create machine learning models that continuously deliver training files to the analytics environment by extracting and pre-processing suitable features. This can be brought to Striim using the open processor component, which facilitates operational decision-making by implementing ML logic to streaming events for gaining real-time insights. Monitoring the fitness of the model and fully automating it through retraining are also possible.

Striim Platform’s Core Capabilities and Benefits

Some of the core features of the Striim platform that enable the above use cases for streaming data integration include:

● Collection of Continuous, Structured, and Unstructured Data: Real-time data of all types is gathered from multiple sources on the Striim platform. These include databases (using low-impact change data capture), log files, cloud applications, IoT devices, and message queues.

● Stream Processing Using SQL: Striim uses static or streaming reference data for applying filtering, transformations, masking, aggregations, and enrichment.

● Monitoring and Alerting Pipelines: Striim enables the real-time visualization of data flow and content while offering delivery validation.

● Real-Time Delivery: Streaming data is distributed in a consumable form to all major targets such as Cloud environments, messaging systems including Kafka, Hadoop, flat files, and relational and NoSQL databases.

You might be interested in: Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming

Some of the key advantages of the Striim streaming platform for unified data integration include:

● Streaming data integration with intelligence using an in-memory platform

● Movement of real-time data across on-prem and cloud environments

● Low-impact CDC for Oracle, HPE NonStop, SQL Server, and MYSQL

● SQL-based in-flight filtering, transformation, aggregation, and enrichment

● drag-and-drop UI for quick deployment and easy integration

● Continuous monitoring of data pipeline and built-in delivery validation

● Can integrate with existing technologies and open source solutions

Indium – Striim Partner to Enable Data Integration

Streaming data integration in Striim acts as the backbone for an enterprise’s data fabric that breaks down data silos and enables the building of an agile and global data environment for tracking, analyzing, and governing data across environments, applications, and users.

Indium is a Striim partner that facilitates connecting legacy and modern solutions to deliver real-time data through intelligent pipelines. It builds a flexible and scalable data integration backbone that connects data from hybrid and multi-cloud environments.

With real-time data integration, organizations can improve the digital experiences for customers through increased responsiveness and customization. Indium facilitates a bespoke development of Striim streaming platform for unified data integration to help businesses leverage their data for enhancing their customers’ digital experience.

The post Use Cases for a Unified Data Integration and Streaming Platform like Striim appeared first on Indium.

While businesses have access to lots of data thanks to the growth in IoT-based devices, cloud, enterprise systems, and so on, they face two problems. One is that the data is in raw format and two, it is stored in multiple systems and multiple formats. As a result, businesses need a solution that can pull structured and unstructured data in one place and convert this data into a unified format to act as the single source of truth.

A Gartner survey for the data integration tools market, titled ‘Adopt Stream Data Integration to Meet Your Real-Time Data Integration and Analytics Requirements’ and published in March 2019, indicates that 47% of organizations require streaming data that can help them build a digital business platform. However, only 12% had an integrated streaming data solution for their data and analytics requirements.

Traditionally, businesses depended on the ETL model – Extract, Transform and Load – but this is run as batch jobs periodically, rendering the data outdated and of limited use for some use cases.

In these times when businesses have to take quick decisions and respond to changing external and internal in a timely manner to remain competitive, depending on the ETL can be limiting to growth.

Real-Time Data Movement with In-Flight Processing

The face of data has changed tremendously today. Data is not only that which is stored in tables but also textual and documents across different formats stored in document stores such as MongoDB, Amazon DynamoDB, Couchbase Server and Azure Cosmos DB. An ETL can transfer data from one database to another, but with unstructured documents stored in these data stores, businesses need in-flight processing and built-in delivery validation along with real-time data movement.

MongoDB, for instance, is a document store where many of the sources are relational, flat, or unstructured. It will require a real-time continuous data processing solution such as Striim to create the necessary document structure as required by the target database.

Striim Features for Data Movement

Striim, an end-to-end, in-memory platform, collects, filters, transforms, enriches, aggregates, analyzes, and delivers big data in real-time. Designed especially for stream data integration, it uses low-impact change data capture to extract real-time data from different sources such as IoT devices, document stores, cloud applications, log files, and message queues and deliver it in the format needed and can deliver to or extract from MongoDB (or equivalent) as required.

With CDC, it delivers to MongoDB one collection per table, inserting, deleting, and updating documents based on the CDC operation, the row tuple contents with metadata, and the fields containing data elements.

It facilitates filtering and transforming data using SQL. Data enrichment is made possible by coupling it with external data in caches. Query output or custom determine the JSON document structure.

Custom transformations are also possible for complex cases with custom processors. While it is possible to achieve granular document updates, moving data from master/detail-related tables into a document hierarchy is also possible.

Benefits of Striim

Some of the key features of Striim that enable businesses to improve operational efficiency and deliver from and to document stores in real-time with in-flight processing for data integrity include:

• Low-impact change data capture from enterprise databases that allows for continuous and non-intrusive ingestion of high-volume data. It can support data warehouses such as Oracle Exadata, Amazon Redshift and Teradata; and databases such as MongoDB, Oracle, SQL Server, HPE NonStop, MySQL, PostgreSQL, Amazon RDS for Oracle, and Amazon RDS for MySQL. It enables data collection in real-time a variety of sources such as logs, sensors, Hadoop and message queues to enable real-time analytics.

• Non-stop data processing and delivery are effected through an inline transformation using processes such as denormalization, filtering, aggregation, and enrichment. This facilitates storing only the relevant data in the required format. A hub and spoke architecture is supported using real-time data subsetting and optimized delivery is enabled in both streaming and batch modes.

• Built-in monitoring and validation allow for non-stop verification of the consistency of the source and target databases. In addition to interactive, live dashboards for streaming data pipelines, it also enables real-time alerts via web, text or email.

Striim makes it possible for businesses to upgrade from ETL solutions to streaming data integration at an extreme scale by providing a wide range of supported sources. Any data can be made available in platforms such as MongoDB in real-time, in the required format to leverage scalable document storage and analysis.

Some of the key benefits of Striim include continuous data movement from a variety of sources with sub-second latency in real-time; a non-intrusive collection of real-time data from production systems with least disruption; and in-flight denormalization and other transformations of data.

Indium – A Striim Partner

Indium Software is a strategic partner of Striim, empowering businesses to make data-driven decisions by leveraging the real-time Big Data Analytics platform. Indium offers innovative data pipeline solutions for the continuous ingestion of real-time data from different databases, cloud applications, etc., leveraging Striim’s highly scalable, reliable, and secure end-to-end architecture that enables the seamless integration of a variety of relational databases. Indium’s expertise in Big Data coupled with the capabilities on the Striim platform enables us to offer solutions that meet the transformation and in-flight processing needs of our customers.

To find out how Indium can help you with your efforts to replace your traditional ETL solutions with a next-gen Striim platform for real-time data movement with in-flight processing, contact us now:

The post Ready to Replace Your Traditional ETL Solutions? Indium can help you use Striim for Real-Time Data Movement with In-Flight Processing appeared first on Indium.

There is an increasing move towards storing data in a hybrid or multi-cloud environment to keep infrastructure costs low while enjoying the benefits cloud offers of flexibility and scalability.

While this has its benefits, such a hybrid environment also brings with it certain limitations. Data is stored in multiple locations and multiple formats and to leverage the data and draw insights for informed decision making, businesses need a unified view.

Data integration is the process by which data from different locations is unified and made usable. With the number of data sources increasing, the need for effective data integration tools is also gaining importance.

With data integration businesses gain:

• Access to a single and reliable version of truth, synchronized and accessible from anywhere
• Access to accurate data that enables effective analysis, forecasting, and decision making

5 Applications of Striim-based Data Integration

A platform such as Striim enables data integration of on-premise and cloud data from Databases, Messaging Systems, Files, Data Lakes, and IoT in real-time and without disrupting operations.

It provides users access to the latest and reliable data from varied sources such as log files, databases, sensors, and messaging systems. Pre-built integration and wizards-based development enables an accelerated building of streaming data pipelines and provides timely insights for improved and data-backed decision making.

The various scenarios where Striim-based data integration can be applied include:

1. Integration Between On-premise and Cloud Data

Businesses migrating data from legacy systems to the cloud can benefit from Striim’s Change Data Capture (CDC). CDC reduces downtime, prevents the locking of the legacy database, and enables real-time data integration (DI) to track and capture modifications to the legacy system, applying the changes to the cloud after the migration is complete.

It also facilitates the continuous synchronization of the two databases. It also allows for data to be moved bi-directionally, with some stored in the cloud and some in the legacy database. For mission-critical systems, the migration can be staggered to minimize risks and business interruptions.

2. Real-time Integration in the Cloud

Businesses opting for cloud data warehouses require real-time integration platforms for real-time data analysis. The data is sourced from both on-prem and cloud-based sources such as logs, transactional databases, and IoT sensors and moved to cloud warehouses. CDC enables ingesting data from these different sources without disrupting data production systems, delivers it to the cloud warehouses with sub-second latency and in a usable form.

Techniques such as denormalization, enrichment, filtering, and masking are used for in-flight processing, which imparts benefits including minimized ETL workload, reduced architecture complexity, and improved regulatory compliance. As synchronizing cloud data warehouses with on-premises relational databases is possible, data is moved to the cloud in a phased migration to reduce disruption to the legacy environment.

3. Cloud Integration for Multi-cloud Environments

Real-time data integration in multiple cloud environments connecting data, infrastructure, and applications improves agility and flexibility to move your data to different data warehouses on different clouds.

4. Enabling Real-time Applications and Operations

With data integration, businesses can run real-time applications (RTA) using on-premise or cloud databases. The functioning of RTAs can seem immediate and current to users because of real-time integration solutions moving data with sub-second latency.

Further, data integration also transforms data, cleans it, and runs analytics, helping RTA further. It can be of use for several applications such as videoconferencing, VoIP, instant messaging, online games, and e-commerce.

5. Anomaly Detection and Forecasting

With real-time data integration, companies can manipulate the IoT data generated by different types of sensor sources, clean it and unify it for further analysis. Among the various types of analytics one can run on a real-time data pipeline, anomaly detection and prediction are important as they enable timely decisions.

These can be of use in many scenarios: for checking the health of machinery and robots in the factories; health of planes, cars, and trucks; cybersecurity to detect and prevent fraudulent transactions, among others.

The use cases are not restricted to the above five. Data integration can support machine learning solutions by reducing the time for cleaning, enriching, and labeling data and ensuring the availability of real and current data. It can help synchronize records across departments, functions and systems and provide access to the latest information.

It can improve an understanding of customers as well as decide the course of marketing strategies. It can also help with faster scaling up and can be a game-changer.

Indium is a Striim implementation partner with more than 20 years of experience in consulting and implementation in leading-edge technologies.

Our team of data scientists and engineers have vast experience in data technologies, integration, and Striim and work with domain experts to create bespoke solutions catering to the specific needs of the customers across industries.

If you have a multi-cloud or hybrid environment and would like to leverage your data stored in different locations more effectively with data integration, contact us now:

The post Five Data Integration Use Cases in 2021 appeared first on Indium.

It is no more only about data processing and creating data pipelines, it is about doing it in real-time. This has created a need for technologies that can handle streaming data and enable a smooth, automated flow of information from input to output as needed by different business users. This growing demand is reflected in the fast-growing demand for Big Data technologies, which is expected to grow from 36.8 billion in 2018 to 104.3 billion in 2026 at a CAGR of 14 %, according to Fortune Business Insights.

Features of a Streaming Data Pipeline

The key elements for a good pipeline system are:

• Big Data compatibility
• Low latency
• Scalability
• Multiple options to handle different use cases
• Flexibility
• Cost-effectiveness

To make it cost-effective and meet the organizational needs, the Big Data pipeline system must include the following features:

• A Robust Big Data Framework with a high volume of storage Apache Hadoop.
• A publish-subscribe messaging system
• Machine learning algorithms to support predictive analysis support
• A flexible, backend storage for result data
• Reporting and visualization support
• Alert support to generate text or email alert

Tools for Data Pipeline in Real-Time

There are several tools available today for creating a data pipeline in real-time, collecting, analyzing and storing several millions of pieces of information for creating applications, analytics, and reporting.

We at Indium Software, with expertise and experience in Big Data technologies, recommend the following 5 tools to build real-time data pipeline:

• Amazon Web Services: We recommend this because of its ease of use at competitive rates. It offers several options such as Simple Storage Service (S3) and Elastic Block Store (EBS) to store large amounts of data which is supported by Amazon Relational Database Service for performance and optimization of transactional workloads. AWS also offers several tools for data mining and processing data. AWS Data Pipeline web enables the reliable processing and moving of data between different AWS compute and storage services. This is a highly available and scalable platform for your real-time data processing needs.

• Hadoop: Hadoop can be effectively used for distributed processing of huge data sets across different clusters of servers and machines parallelly. It uses MapReduce to process the data and Yarn to divide the tasks, responding to queries within hours if not seconds. It can handle Big Data volumes, performing complex transformations and computations in no time. Over time, other capabilities have been built on top of Hadoop to make it a truly effective software for real-time processing.

• Kafka: The open-source, distributed event streaming platform Apache Kafka enables the creation of high-performance data pipelines, data integration, streaming analytics, and mission-critical applications. Kafka Connect and Kafka Streams are two components that help in this. Businesses can combine messages, data and storage using Kafka whose other valuable components such as Confluent Schema Registry allows them to create the appropriate message structure. Simple SQL commands empower users to filter, transform and aggregate data streams for continuous stream processing using ksqlDB.

In addition to being used for batch applications and real-time, Kafka helps integrate with REST, files and JDBC, the non-event-streaming paradigm for communication. Kafka’s reliable messaging and processing with high availability makes it apt for small datasets such as bank transactions. The other two critical features, zero data loss and exactly once semantics, makes this ideal for real-time data pipeline creation along with streaming data manipulation capabilities. On-the-fly processing is made possible with Apache Kafka’s Streams API, a powerful, lightweight library.

• Spark: A popular open-source real-time data streaming tool promises performance and lowers latency. Spark Streaming enables the merging of streaming and historical data and supports Java, Python, and Scala programming languages. It also provides access to the various components of Apache Spark.

• Striim: Striim is fast becoming popular for streaming analytics and data transformations because of it being easy to implement and user-friendly. It has in-built messaging features to send alerts, ensures secured data migrations between, ease of data recovery in case of failures and agent-based approach for highly secured databases.

Indium has successfully deployed these technologies for its various data engineering projects for its customers across different industries, including banking, mobile app development and much more.

We have the experience and expertise to work on the latest data engineering technologies to provide the speed, accuracy and security that you desire for building data pipelines in real-time. Contact us for your streaming data engineering needs.

The post Top 5 Technologies to Build Real-Time Data Pipeline appeared first on Indium.

It allows querying data as per the bank’s requirements and brings together enterprise data warehousing and Big Data analytics. Based on these insights, banks can devise strategies for improved efficiencies in operations and development of products for better customer service.

Striim for CDC

A platform such as Striim enables the transfer of data from heterogeneous, on-premise data warehouses, databases, and AWS into Azure Synapse Analytics with in-flight transformations and built-in delivery validation. This helps with operational decision making on a continuous basis.

For the exercise to be really fruitful in today’s world of instant response, it is necessary for the data being transferred to be as current and close to the source database on the core banking system. A platform like Striim enables this data integration from the source table to the target using Change Data Capture (CDC).

CDC allows for data from on-prem sources, regardless of whether it is an RDBMS, No-SQL, or any other type, to a Synapse table or ADLS Gen-2 (Azure Data Lake Store Generation – 2) to be created and updated in near real-time. It doesn’t hit the source database directly.

Instead, it captures all the transactions, be it an update, insert, or delete, from the source database on-prem on a daily basis from the log and gets it generated and duplicated on the target database

This way, the performance of the source database is not affected while there is access to data on the cloud in near real-time for analysis and response.

Advantage Striim

One of the factors that make Striim the most desired CDC tool is its price point while being feature-rich. An evolving tool, it also allows for features such as UDF (User Defined Function) that can be plugged in on the fly. It allows for data manipulation and querying based on the unique needs of the bank. The icing on the cake is the reporting feature with live dashboards and a diverse set of metrics for effective data monitoring.

Its built-in monitoring and validation features include:

• Ensure consistency through continuous verification of the source and target databases
• Enable streaming data pipelines with interactive, live dashboards
• Trigger real-time alerts via web, text, email

By powering the data integration of the on-prem database of the core banking system with Azure Synapse using Striim, banks can ensure continuous movement of data from diverse sources with sub-second latency.

It is a non-intrusive way of collecting data in real-time from production systems without impacting their performance. It also allows for denormalization and other transformations on data-in-motion.

The data warehouses Striim supports include:

• Oracle Exadata
• Teradata
• Amazon Redshift

Databases:

• Oracle
• SQL Server
• HPE NonStop
• MySQL
• PostgreSQL
• MongoDB
• Amazon RDS for Oracle
• Amazon RDS for MySQL

Striim can integrate data in real-time data from logs, sensors, Hadoop, and message queues to real-time analytics.

Indium – A Striim Enabler

Indium Software is a two-decade-old next-generation digital and data solutions provider working with cutting edge technologies to help banks and traditional industries leverage them for improving their business process, prospects, and efficiencies.

We can help identify the tables in the core banking system that need to be replicated on the target Synapse and set up the Striim platform for smooth integration. Leaders in implementing Striim, we have successfully lead several such integrations across sectors. Our team has cross-domain experience and technology expertise which helps us become partners in the truest sense.

If you would like to leverage cloud and analytics through Striim, contact us here:

The post Striim-Powered Real-Time Data Integration of Core Banking System with Azure Synapse Analytics appeared first on Indium.

But, that’s not all. There is also an explosion of real-time data coming in, and unless you adopt “Streaming Analytics” to instantly draw insights, you may miss out on spotting opportunities or tackling threats ahead of time. The point is, running batch-mode analytics alone is no longer sufficient.

“Streaming Analytics” can also help mitigate risks from fraud or security breaches. Moreover, one of the key advantages of gathering real-time actionable insights revolves around the ability to capture data that changes.

The Striim Platform automates the process of capturing time-sensitive insights to provide immediate intelligence that can impact the following:

1. Spot critical fraud or security breaches
2. Spot major changes or trends than can in-turn help you spot opportunities and act instantly with modified marketing campaigns or strategies
3. Identify risks and critical events that can impact both short-term and long-term strategy

Needless to add, Striim is designed as an enterprise-grade platform, one that is highly secure, reliable and scalable.

At Indium Software, we’re an authorized implementation partner of Striim.

We’ve worked with a range of clients including banks, financial institutions and retail & e-commerce companies, helping them with Striim implementation. Recently, we worked with one of the world’s leading banks, helping its digital banking division with Real Time data integration using Striim. We had to move a massive database from Oracle to GCP, with a Striim agent handling Change Data Capture (CDC) and real-time integration that was highly secure.

Potential Use Cases for Striim Implementation

The potential of use for this analytical application is unlimited. From the energy sector to banking and financial, ecommerce, airlines and healthcare, real-time analytics can help in improving service levels, strategy formulation as well as prevent potential threats to almost any business with massive real-time data.

• It can be used in the energy sector to capture power outages – and then help with preventing them or restore services on priority – by using real-time intelligence
• In the banking, insurance & financial sector, it can help enable risk-based, real-time policy pricing to reduce exposure; detect and prevent fraud, AML compliance; improve regulatory compliance; provide bespoke solutions to customers based on their real-time search data; and streamline ATM operations through remote monitoring and predictive maintenance
• In the transport and logistics sector, it can provide greater visibility into operations in real-time; ensure timely delivery and reduce fuel costs by optimizing fleet routes and planning staff utilization better; implement predictive maintenance and thereby extend the lifespan of the assets;  enable real-time tracking of vehicles; improve warehouse capacity utilization through real-time inventory data analytics
• For the aviation sector, use cases revolve around getting real-time updates on weather, flight delays and other such events to optimize crew and staff planning and flight schedules; track aircraft parts and rapidly submit work orders; improve real-time staffing decisions depending on actual passenger load; reduce immigration and customs lines; provide relevant and meaningful rewards for customer loyalty
• As you can see from above, the use cases are endless. The key aspect is to run streaming analytics on a proven, secure, scalable platform like Striim.

The ‘Striim – Indium Software’ Value Proposition

Striim uses a combination of filtering, multi-source correlation, advanced pattern matching, predictive analytics, statistical analysis and time-window-based outlier detection to aggregate all relevant data.

By querying the streaming data continuously, it quickly and accurately identifies events of interest and provides a deep perspective into operations by performing in-flight enrichment.

It sends automated alerts, triggers workflows, publishes results to real-time, interactive dashboards and distributes data to the entire enterprise.

Striim continuously ingests data from a variety of sources such as IoT and geolocation. It uses advanced pattern matching, predictive analytics and outlier detection for comprehensive streaming analytics. The analytical applications can be easily built and modified using SQL-like language and wizards-based development.

Indium Software, an authorized implementation partner of Striim, has deep expertise and experience in leveraging the Striim platform for the following processes:

• Real Time Data Integration
• Hybrid Cloud Integration
• Streaming Analytics
• GDPR Compliance
• Hadoop and NoSQL Integration

A cross-domain expert with over 20 years of experience in several industries such as retail, BFSI, e-commerce, healthcare, manufacturing, gaming among others, Indium Software is well-positioned to handle a wide range of Big Data services across Data Engineering and Data Analytics.

Recently, we completed a Data Integration project for a leading bank, helping move their data from Oracle database to Postgres in Google Cloud Platform.

The architecture implemented included effective data monitoring and customised visualization of the streaming data. Additionally, alerts were created when multiple data pipelines were being accessed simultaneously.

Additionally, Indium helped a client with the implementation of Striim in their messaging queue platform. With this setup, the client could stream data in their Kafka queue and write data into the Kafka logs using Kafka writer, which could then be consumed by multiple downstream systems and applications

The Indium team aided in supporting another customer through an open processor (custom scripts) in Striim to provide a data audit feature for every transaction hitting the database. The changes were then updated in a log database, enabling tracking of the data change, for example insert, delete and update. Additionally, our team created another open processor in moving the current system time forward by 7 hours, before replicating the timestamp column in the database.

Give us a shout, if your business generates real-time data. We’ll seamlessly create an automated process to draw insights from this real-time feed. We can also certainly help with any of the other aspects of Striim implementation.

The post Real-Time Data Integration using Striim appeared first on Indium.