Smart Data Pipelines for Ever-Changing Business Needs

The world today is moving fast, and requirements changing constantly. Businesses need to respond in real-time to improve customer delight and become efficient to become more competitive and grow quickly. In 2020, the global pandemic further compelled businesses to invest in data and database technologies to be able to source and process not just structured data but unstructured as well to maximize opportunities. Getting a unified view of historical and current data became a challenge as they moved data to the cloud while retaining a part in on-premise systems. However, this is critical to understand opportunities and weaknesses and collaborate to optimize resource utilization at low costs.

The concept of the data pipeline is not new. Traditionally, data collection, flow, and delivery happened through batch processing, where data batches were moved from origin to destination in one go or periodically based on pre-determined schedules. While this is a stable system, the data is not processed in real-time and therefore becomes dated by the time it reaches the business user.

Check this out: Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming

Stream processing enables real-time access with real-time data movement. Data is collected continuously from sources such as change streams from a database or events from sensors and messaging systems. This facilitates informed decision-making using real-time business intelligence. When intelligence is built in for abstracting details and automating the process, it becomes a smart data pipeline. This can be set up easily and operates continuously without needing any intervention.

Some of the benefits of smart data pipelines are that they are:

● Fast to build and deploy

● Fault-tolerant

● Adaptive

● Self-healing

Smart Data Pipelines Based on DataOps Principles

The smart data pipelines are built on data engineering platforms using DataOps solutions. They remove the “how” aspect of data and focus on the 3Ws of What, Who, and Where. As a result, smart data pipelines enable the smooth and unhindered flow of data without needing constant intervention or building or being restricted to a single platform.

The two greatest benefits of smart data pipelines include:

● Instant Access: Business users can access data quickly by connecting the on-premise and cloud environments using modern data architecture.

● Instant Insights: With smart data pipelines, users can access streaming data in real-time to gain actionable insights and improve decisions making.

As the smart data pipelines are built on data engineering platforms, it allows:

● Designing and deploying data pipelines within hours instead of weeks or months

● Improving change management by building resiliency to the maximum extent possible

● Adopting new platforms by pointing to them to reduce the time taken from months to minutes

Smart Data Pipeline Features

Some of the key features of smart data pipelines include:

Data Integration in Real-time: Real-time data movement and built-in connectors to move data to distinct data targets become possible due to real-time integration in smart data pipelines to improve decision-making.

Location-Agnostic: Smart Data Pipelines bridge the gap between legacy systems and modern applications, holding the modern data architecture together by acting as the glue.

Streaming Data to build Applications: Building applications become faster using smart data pipelines that provide access to streaming data with SQL to get started quickly. This helps utilize machine learning and automation to develop cutting-edge solutions.

Scalability: Smart data integration using striim or data pipelines help scale up to meet data demands, thereby lowering data costs.

Reliability: Smart data pipelines ensure zero downtime while delivering all critical workflows reliably.

Schema Evolution: The schema of all the applications evolves along with the business, ensuring keeping pace with changes to the source database. Users can specify their preferred way to handle DDL changes.

Pipeline Monitoring: Built-in dashboards and monitoring help data customers monitor the data flows in real-time, assuring data freshness every time.

Data Decentralization and Decoupling from Applications: Decentralization of data allows different groups to access the analytical data products as needed for their use cases while minimizing disruptions to impact their workflows.

Benefit from indium’s partnership with striim for your data integration requirements: REAL-TIME DATA REPLICATION FROM ORACLE ON-PREM DATABASE TO GCP

Build Your Smart Data Pipeline with Indium

Indium Software is a name to reckon with in data engineering, DataOps, and Striim technologies. Our team of experts enables customers to create ‘instant experiences’ using real-time data integration. We provide end-to-end solutions for data engineering, from replication to building smart data pipelines aligned to the expected outcomes. This helps businesses maximize profits by leveraging data quickly and in real-time. Automation accelerates processing times, thus improving the competitiveness of the companies through timely responses.

The post <strong>Why You Should Use a Smart Data Pipeline for Data Integration of High-Volume Data</strong> appeared first on Indium.

All these processes together are called Data Integration, and it makes data discovery, preparation, and combination effortless.

AWS Glue is a serverless data integration service that cuts down the data integration time from months to minutes and does not require infrastructure to be set up or managed. Users pay only for the resources consumed in a pay-per-use model.

AWS Glue Features

This time- and cost-effective service provides visual and code-based interfaces. Its features include:

• AWS Glue Data Catalog: Data discovery is enabled for all data assets located anywhere using a persistent metadata store in the Data Catalog. It helps manage the AWS Glue environment by providing definitions for tables, jobs, and schemas. Automatic computation of statistics and registers partitions makes data queries efficient and cost-effective. A comprehensive schema version history helps trace all the changes to your data over time.
• It also facilitates automatic schema discovery using crawlers that connect source or target data store, passing through a prioritized list of classifiers that determine the schema. The metadata is created in the tables in the Data Catalog and used for authoring the ETL jobs. The crawlers can be scheduled to run as needed, on-demand, or triggered during an event, ensuring up-to-date metadata.
• AWS Glue Schema Registry: Registered Apache Avro schemas are used at no additional charge for the validation and control of the evolving streaming data. The Schema Registry leverages Apache-licensed serializers and deserializers to integrate with Java applications, which helps to improve data quality and perform compatibility checks that govern schema to protect in case of unexpected changes. The schemas stored within the registry can also be used for creating or updating AWS Glue tables and partitions.
• AWS Glue Studio: Authoring ETL jobs for distributed processing that are highly scalable is now possible even for those who are not experts in Apache Spark. A drag-and-drop job editor allows defining and automatically generating the code in Scala or Python for the ETL process. It also facilitates building complex ETL pipelines to be run at a predetermined time, on-demand, or triggered during an event. Multiple jobs can be run parallelly or based on specific dependencies across jobs for building complex ETL pipelines. Amazon CloudWatch records all logs and notifications, allowing monitoring and being alerted in case of a failure from a central service.
• AWS Glue Elastic Views: This allows you to view data stored in different types of AWS data stores in AWS in any target data store you choose. Queries written using PartiQL can be used to create materialized views, which can be shared with other users. AWS Glue Elastic Views automatically updates target data stores in case of any changes to source data stores.
• AWS Glue DataBrew: Data analysts and scientists do not need to write code to clean and normalize data but can use the point-and-click visual interface. Data can be visualized, cleaned, and normalized directly from the data warehouses, data lakes, and databases, such as Amazon S3, Amazon Aurora, Amazon Redshift, and Amazon RDS. It offers more than 250 built-in transformations for combining, pivoting, and transposing the data. Saved transformations can also be applied directly to new incoming data to automate data preparation tasks.

Benefits of AWS Glue

AWS Glue accelerates data integration through automation of tasks such as discovery by crawling through data sources to identify the different formats of data and suggest schemas for storing the data. Data transformation and loading processes can also be automated. It enables running and managing several ETL jobs and combining and replicating data across multiple sources using SQL. AWS Glue also facilitates collaboration on related tasks such as extraction, cleaning, combining, loading, normalization, and running scalable ETL workflows.

Being serverless, users do not have to set up or manage infrastructure. Resources are also automatically provisioned, configured, and scaled based on need, with costs calculated based on usage.

● AWS Glue can be used to build ETL pipelines based on need.

● By creating a unified catalog, it helps find data from different data stores.

● No coding is required for creating, running, and monitoring ETL jobs.

● It empowers business users to prepare visual data for exploring data.

● Materialized views can be built for combining and replicating data.

Indium Software for Data Integration with AWS Glue

Indium Software is an AWS partner offering expertise across the entire range of AWS solutions, including AWS Glue. Our team of experienced developers and data scientists work closely with our customers to enable transforming ideas into business value quickly.

Indium leverages its more than two decades of experience working across domains and technologies to provide innovative solutions to accelerate transformations. A cross-functional team assesses your needs, designs a bespoke solution, and implements it on time and within budget to ensure optimal performance.

The post AWS Glue for Serverless Data Integration Services for Analytics and Machine Learning 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 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.

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