An Ocean of Data: It is all about connecting the right data in the end.
Investing in Big Data is, of course, a big investment and big decision, but only with the right data comes the information that’s needed to fuel your success. Big data, coupled with data analytics, is poised to offer organizations favorable opportunities for better efficiency and productivity. We, Aspire Systems, help you harness those inaccessible, unfragmented pieces of data that are somewhere hidden in your pool of unstructured data across your organization to unlock business value.
Segments We Serve
Retail
Insurance
Corporate and retail banking institutions
Audit
Educational institutions
Manufacturing
Food Industry
Our Services
Data Lake Implementation
Integration of Streaming Data from IOT and Mobile
Ingestion of Unstructured data from social media and public sites
Modernization of Legacy ETL packages and DWH
Building AWS Lake formation
Homogeneous, Heterogeneous, and cross-cloud migrations
Data Lake Implementation
As a leading big data services provider across the world, we understand that every organization has unique data needs and requirements. That's why we offer customized data lake integration and services tailored to your business and projects. Our pool of data lake experts works closely to understand your needs and engineer a solution that meets your specific requirements. With our modern technologies and tools, your business gets a scalable and flexible data lake that can translate hidden data into insights and business strategies that are paramount in creating a conducive business environment for you. We focus on all the three clouds and on-premises big data platforms, including Azure, AWS, GCP, and In-house Cloudera for building Data Lakes. Our experienced and certified data engineers can build a variety of data lakes to meet each customer's specific needs while delivering optimal performance and significant cost savings.
Data Lake implementation services
- Legacy ETL Modernization
- Legacy DWH Modernization
- Cost-saving Data Lakes
- Data Lake house Implementation
- Data Lake formation
Integration of Streaming Data from IOT and Mobile
At Aspire, we have best practices to Integrate streaming data from IOT and Mobile devices. Apache Kafka is a publish-subscribe based durable messaging system. Kafka is a distributed system consisting of servers and clients that communicate via a high-performance TCP network protocol. A Kafka cluster consists of one or more servers (Kafka brokers) running Kafka. Producers are processes that push records into Kafka topics within the broker. A consumer pulls records off a Kafka topic. Management of the brokers in the cluster is performed by Zookeeper.
Business Benefits:
- Write and read streams of events, including continuous import/export of your data from other systems.
- Store streams of events durably and reliably for as long as you want.
- Process streams of events as they occur or retrospectively.
Five types of workloads and each corresponds to a specific API.
Kafka Producer API:
Applications directly producing data (ex: clickstream, logs, IoT).
Kafka Connect Source API:
Applications bridging between a datastore, which we don’t control, and Kafka (ex: Postgres, MongoDB, Twitter, REST API).
Kafka Streams API:
Applications wanting to consume from Kafka and produce back into Kafka, also called stream processing. We can use this when it is required to write complex logic for the job.
Kafka Consumer API:
Read a stream and perform real-time actions on it (e.g., send email…)
Kafka Connect Sink API:
Read a stream and store it into a target store (ex: Kafka to S3, Kafka to HDFS, Kafka to MongoDB, etc.)
The below use cases show how the produced events are consumed and processed:
Use case 1:
Real-time alerting on a single event: Monitor assets and people and send an alert to a controller, mobile app, or any other interface if an issue happens.
Use case 2:
Continuous real-time aggregation of multiple events: Correlation data while it is in motion. Calculate average, enforce business rules, apply an analytic model for predictions on new events, or any other business logic.
Use case 3:
Batch analytics on all historical events: Take all historical data to find insights, e.g., for analyzing issues of the past, planning future location requirements, or training analytic models.
Ingestion of Unstructured data from social media and public sites
Our cloud-based business intelligence (BI) service offers an efficient solution for businesses that want to extract valuable insights from unstructured data sources, such as social media and public websites. Our cutting-edge data ingestion capabilities make it simple to collect, integrate, and analyze large amounts of unstructured data, enabling you to gain a deeper understanding of customer behavior, brand sentiment, and industry trends. Our service supports a wide variety of social media platforms and public sites, including Twitter, Facebook, LinkedIn, news sites, blogs, and more. By leveraging this service from Aspire Systems, you can quickly convert unstructured data into actionable business insights and make informed business decisions.
Here is how we do it:
- We collect and integrate unstructured data from various sources, including social media platforms and public websites.
- The collected data is then transformed into a structured format using natural language processing (NLP) techniques, which identify key information and sentiments within the data.
- The transformed data is loaded into a cloud-based BI platform where it can be analyzed, visualized, and used to gain insights into customer behavior, brand sentiment, and industry trends.
- The platform provides various tools and features to slice and dice the data, perform advanced analytics, and generate reports.
- This service offers a comprehensive and seamless solution for businesses to extract valuable insights from unstructured data sources, allowing them to make informed business decisions.
Modernization of Legacy ETL packages and DWH
Aspire has successfully modernized various ETL packages across multiple clouds, including the Azure cloud. Modernizing ETL processes can be a cost-effective solution for companies looking to streamline their IT infrastructure. It enables businesses to revamp their business processes and integrate data from enterprise applications with external systems such as merchants and channels in real-time, making the process more intuitive and seamless to use.
A data roadmap enables data-driven decision-making by providing accurate, timely, and relevant information that supports informed business decisions. It also provides a platform for innovation by identifying new data sources, analytics, and technologies that can transform the business's operations. In short, a data roadmap is essential for businesses to maximize the value of their data and achieve long-term success.
Recommended Services
- Cloud Platform – Azure Cloud Platform,
- Synapse pipeline for Orchestration and data ingestion
- Enterprise storage – Data Lake storage for raw data and processed data sets.
- Databricks for in-memory data processing
- Enterprise Warehouse– Azure Synapse Analytics
- Cloud Identity and secure-AAD, Azure key vault
- Cloud monitor- Azure Monitor
- Microsoft Purview - Data governance, data discovery, sensitive data classification, and end-to-end data lineage
- Azure DevOps for CI/CD integration
- Logic apps for email triggering
AWS Data Lake Formation
We at Aspire have built variety of Data lakes in AWS S3 for many Customers. Lake formation is the concept of meta data layer and data governance on top of data lakes. We have collaborated with some leading conglomerates across a wide variety of business sectors and successfully created data lake formations that have met their business needs and requirements.
Business Benefits
- Data streamlining and ingestion.
- Data de-cluttering.
- Data cataloguing and indexing.
- Data analysis.
- Top-notch data security at database and table level.
- Easy and controllable data access to users from a central location.
- Data flows orchestrations
AWS Native Tools and services for Lake formation:
- AWS Lake Formation to simplify the creation, security, and management of your data lake
- AWS Glue to orchestrate jobs with triggers to transform data using the AWS Glue transforms.
- AWS IAM (Identity and Access Management) to secure data using Lake Formation permissions to grant and revoke access.
- The Data Catalogue as the central metadata repository across several services.
- Amazon Athena to query data.
- Amazon SageMaker to analyze data.
- AWS Glue machine learning transforms and cleanses data.
Homogeneous, Heterogenous and Cross-cloud Migrations
Homogeneous migration:
Any upgrade or higher version migration of the same database falls under this category. It is a Database migration from source databases to target Database where the source and target databases are of the same RDBMS from the same provider. All stored procedures, Functions, DB Objects and other DB related Objects will be migrated to the Target system. We at Aspire has best practices for all Data migrations, which is more optimized, best in the industry with optimal time frame and huge cost savings.
Heterogenous Migration:
Heterogeneous migration, a specialized cloud BI service that helps organizations transfer data and applications from one IT environment to another, where the source and destination environments have different hardware, software, operating systems, and configurations.
If the Source DB and Target DB are two different Vendor products, then this migration is called Heterogeneous migration. We at Aspire has best practices for all Data migration. The Best Practices include Pre-Migration, Migration, Post-Migration steps and support. We recommend tools and Technologies for high volume migrations, On-prem to Cloud migrations, Phased Migrations, so-on and so forth. We have done Migrations for Educational domain, Banking, Retail, Insurance and other Industries.
Cross cloud Migrations:
With our cross-cloud migration solutions, companies can transfer data and applications between different cloud providers or between on-premises environments and the Cloud. If the data from one or more Cloud provider moves to a target cloud provider with secured data sharing and or across regions it is called as a Cross cloud Migration. We at Aspire moved data securely from AWS S3 to Azure and Azure to GCP and Vice versa. All these data movements are hybrid with a secured way.
We, at Aspire Systems, use specialized tools and techniques to transfer the data and applications, ensuring that the transfer is smooth and secure. With our proven experience in working on cross-cloud migrations for a wide range of business sectors, we are one of the reliable partners in migrating data and modernizing ETL data movements across the Clouds.
