Way2Smile specializes in handling a huge volume of data by offering innovative and futuristic solutions for our clients. Our rich and experienced team can work on any type of data requirements and make them possible.
We implement the strategy through the following way:
We come up with a full-fledged dashboard that is built with all the advanced features which are beneficial for easy access by anyone. Added, we are responsible for changing your data into useful formats for easy perception.
Our expert team is here to provide you with a huge volume of data being converted to attractive formats by leveraging top-tier technologies presently. Right from common excel sheets, APIs, to cloud storage our featured application comes in a way for easy usage.
Additionally, we also integrate connectors in between the systems for easy connection with the data. Hence, managing data between distributed systems efficiently without any hurdles.
With our feature-loaded dashboard, one can easily view, access, and analyze the real-time data by eliminating the current challenges. With our rich experience, we are here to assist you in building a solution that has the capability of handling multiple complexities simultaneously.
Way2Smile also makes sure that the designed system is out of errors and can perform all business processes with greater availability. One can even look for the recent event data and is retrieved in seconds by scaling a large amount of data or the data points.
We, people, are here to develop a top-notch data pipeline that can be used by different teams in your enterprise based on your business needs.
Our team is here to store all your structured and unstructured data in a single repository. With this, data scientists and other engineers in your enterprise can access/ retrieve the data of their choice with their own frameworks or analytical tools.
Added to the list, our application is built in a way that it can offer you advanced queries capability which can further assist you in creating new business models which are simple to customize based on your customer needs and look for different revenue ways.
Moreover, we also follow enterprise-grade security protocols to ensure that the data is fully integrated into your existing system.
Big data DevOps ManagementIf you feel inconsistency is a complex thing for you, get started with integrating DevOps for your Big Data infrastructure which is a perfect choice here. Being in the industry for more than 5 years, we have talented professionals who can handle this.
We have assisted numerous clients in the industry dealing with Big Data solutions and deliver the outcomes exactly as expected. Way2Smile also ensures that our solutions can uprise your performance by utilizing Hadoop clusters.
A fully reliable systemReliability is the factor that is the basic and mandatory expectation of every client. Since we integrate data security and features together, we ensure your data is secure and accurate outcomes are produced accurately.
Initially, we check for the guidelines in the industry before implementing them and make sure they are followed rightly in our development process.
Simplified & Top-notch DashboardsBeing a pioneer in offering Data Engineering Services, Way2Smile ensured the feature-packed dashboard comes with interactive features and visuals that make your team access them whenever needed without any complexities.
We have integrated multiple features such as auto-refreshing of data in the present time to make futuristic decisions easily without any further delays. Added, you don’t need a human intervention here which also eliminates the probability of errors.
While dealing with the Fintech industry, we ensure our solutions are perfectly blended with security which helps organizations in dealing with a huge amount of data at a time. This can, in turn, rule out the risks associated with data security.
Your business can now easily view the future of your business with the present data. For instance, you can create discounts, coupons, and other exciting offers based on the current data with which you can easily gear up your business.
Since Agriculture is the key industry for every country, we provide additional concern when dealing with Agricultural solutions. We provide superfine Data Engineering Solutions in the market which helps organizations to make predictive analytics.
When speaking about our unique solutions for the Automotive Industry, we proffer outcomes that assist businesses to deal with a large amount of data which is implemented with Data Analytics which further creates high-end information.
Initially, we check for the current challenges in the Real Estate Industry, come up with new business tactics with Data solutions, and implement them perfectly. This can, in turn, enhance the performance of every business involved here.
We provide futuristic Telecom solutions where we are responsible for integrating our data from client calls along with various information such as network, records, equipment, server logs, and other details for future-based decision making.
As there are multiple challenges associated with the Transportation Industry, we figure our best possible ways with our Data Solutions to tackle these problems. We make use of next-gen technologies and algorithms to make this possible.
Being a client, you can now easily manage and analyze the data sets which are generated by smart meters and grid equipment to reduce costs and improve the efficiency of the energy consumption in the present society with tech solutions.
The Main objective of this process is to automate the data which is available in delta lake and ensure the high-quality analytical data from various sources.
The Main objective of this ETL process to data will be extracted from 3 types of sources,and ingest those raw data into Azure Synapse and transform it to load Facts and Dimension tables.
In this ETL process data will be extracted from 3 types of sources,and ingest those raw data into Snowflake and transform it to load Facts and Dimension tables.
The Main objective of this ETL process to data will be extracted from 3 types of sources,and ingest those raw data into Azure Synapse and transform it to load Facts and Dimension tables.
The Main objective of this process is to automate the data which is available in delta lake and ensure the high-quality analytical data from various sources.To make this process easily scalable and check the quality of data using Great Expectation which helps to reduce the workload of data scientists and manual process.
To provide the scorecard of data quality in Power BI which provides helpful insights to improve the quality of data.
The diagram below shows a high-level architectural design for the data quality analyzing process using Great Expectations, Azure delta lake,Azure blob storage,Azure databricks and Power BI.
The Main objective of this ETL process to data will be extracted from 3 types of sources,and ingest those raw data into Azure Synapse and transform it to load Facts and Dimension tables.
Ingest pipeline design describes how the raw data transformed from source systems to sink (Synapse) and shows how Azure Data Factory activities are used during the data ingestion phase.
Below diagram shows a high-level design for copying data from sources ARGUS - SQL server, SAP ECC, and flat files to target data warehouse (Sink) on cloud Azure Synapse Analytics.
In this process configuration driven framework is copying the data from sources to target using a csv file which consists of source & destination schema,table and path info which is stored in ADLS2. using these configuration files to be read and passed to the pipeline dynamically.
Pipeline reads data from config file to get database, tables, path
Using ADF linked service and data set objects, copy data from source to sink
All raw data ingestion load is configured to perform “Truncate and load”
Pipeline auto-creates tables directly based on source column names and data types
Data transformation describes how raw data gets transformed and restructured into facts and dimension tables as per the designed data model using Star schema.
data transformation will be implemented using two approaches
Pipeline reads data from config file to get database, tables, path
Using ADF Data Flow Activity to transform & load data into Synapse
Both our Dim and Fact implement using Slow changing dimensional type1 approach in TSQL.
Dimension And Fact Load:
Step 1: Create SQL views for dimension that holds transformation logic
Step 2: Create Store Procedure to perform Inserts / Updates for loading SCD Type 1 dimensions. This procedure takes source table, target table names and primary key column as inputs
Step 3: Create and load Dimensional tables from Staging VIEWS and Store Procedure
In this ETL process data will be extracted from 3 types of sources,and ingest those raw data into Snowflake and transform it to load Facts and Dimension tables.
Ingest pipeline design describes how the raw data transformed from source systems to sink (Snowflake) and shows how Azure Data Factory activities are used during the data ingestion phase.
Below diagram shows a high-level design for copying data from sources ARGUS - SQL server, SAP ECC, and flat files to target data warehouse (Sink) on cloud Snowflake.
In this process configuration driven framework is copy the data from sources to target using a csv file which consists of source & destination schema,table and path info which is stored in ADSL2.using these configuration files read and passed to pipeline dynamically.
Pipeline reads data from config file to get database, tables, path
Using ADF linked service and data set objects, copy data from source to sink
All raw data ingestion load is configured to perform “Truncate and load” method
In Snowflake, ADF does not provide auto-create tables option. Table creations will be created using DDL scripts
Data transformation describes how raw data gets transformed and restructured into facts and dimension tables as per the designed data model using Star schema.
data transformation will be implemented using two approaches
Pipeline reads data from config file to get database, tables, path
Using ADF Data Flow Activity to transform & load data into Synapse
Both our Dim and Fact implement using Slow changing dimensional type1 approach in TSQL.
Dimension And Fact Load:
Step 1: Create SQL views for dimension that holds transformation logic
Step 2: Create Store Procedure to perform Inserts / Updates for loading SCD Type 1 dimensions. This procedure takes source table, target table names and primary key column as inputs
Step 3: Create and load Dimensional tables from Staging VIEWS and Store Procedure
The Main objective of this ETL process to data will be extracted from 3 types of sources,and ingest those raw data into Azure Synapse and transform it to load Facts and Dimension tables.Transformed from source systems to sink (Synapse) and connect the sql dedicated pool to power BI for generating reports based on their business needs.
Below diagram shows a high-level architectural design for ETL using azure data factory,azure apache spark and Power BI in Azure Synapse Analytics.
For fintech application data needs to be extracted from multiple sources such as postgreSQL,mongodb and flat files which ingest raw data into azure data lake gen 2.Since data ingestion will be huge between postgreSQL and synapse.
In our implementation ,handled with 3 different approach
1.Ingest postgreSQL tables to Azure data lake storage gen2 and copy same data from ADLS gen2 to SQL dedicated pool
2.For Ingesting mongodb transformation data will be in BSON format.these data will convert and flatten the relational database format using apache spark and migrate the same data into SQL dedicated sql pool.
3.Ingest Flat files into Azure data lake storage gen2 and convert into external view to SQL dedicated pool
Dimension And Fact Load:
Step 1: Create SQL views for dimension that holds transformation logic
Step 2: Create Store Procedure to perform Inserts / Updates for loading SCD Type 1 dimensions. This procedure takes source table, target table names and primary key column as inputs
Step 3: Create and load Dimensional tables from Staging VIEWS and Store Procedure
Setting up the power BI tools and connecting with synapse for designing the reports based on business requirements.
To ensure the data is safe, secure, and make it available anytime, we prepare a backup for all your files and data with encryption techniques. Added, we also make sure that we are updated with the popular security practices and the network is also protected with various factors like firewalls, intrusion-detection systems, etc.
We handle data from multiple sets of industries be it a complex or a simple one in order to keep up with the latest trends and technologies by also considering the safety protocols as per the business requirements of the organization.
Undoubtedly, Yes! We are well-equipped with a good infrastructure which every client expects. You can also utilize our cost-effective software implementation easily.
We make use of two types of models Predictive and Descriptive. With Predictive Models, businesses can predict the future based on the present data.
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Dubai - United Arab Emirates
Property No:3D, Gold Tower, Cluster I, JLT Dubai.
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