With niche expertise in handling high volume of data, W2S Solutions offers solutions that are scalable, unique and result driven for clients. Our highly skilled Data Engineers work on complex requirements and make it true.
We implement the development process with the following:
We proffer a simple yet intuitive dashboard that comes with all the latest features which are helpful for everyone who access it. Moreover, we completely transform the data into useful formats for easy representations.
Our team is here to showcase massive amounts of data into attractive formats by implementing next-generation technologies. Be it a common excel spreadsheet, API’s, or cloud storage, the designed application comes in handy to use.
We are also responsible for integrating connectors between various systems to help with easy connections between the data. Thereby, we also manage data between distributed systems effectively without any complexities.
One can now access, view, and analyze the real-time data by ruling out the challenges. Our professionals are here to build you a solution that is capable of handling complex systems with our automation process.
We also ensure that the system is free from errors and are capable of performing business processes with high availability. One can query the recent event data in seconds and scale a huge amount of data or the data points. We are here to create a high-end pipeline that can be utilized by diverse teams in your organization depending on their project needs.
Our experts will store all your structured and unstructured data into a single repository. With this, data scientists and other business professionals in the organization can access data with their choice of own analytical frameworks and tools.
Additionally , we have also designed the application in such a way that it can also offer you advanced query capabilities which can help you with developing new models which are easy to customize based on customer expectations and figure out new revenue streams. We also follow enterprise-grade security measures to make sure data is integrated easily into your existing system.
Big data DevOps managementIf consistency is an issue for your application, you can get started with combining DevOps for your Big Data infrastructure which can be the best possible solution. Having explored this Data engineering for more than 5 years, We have skilled professionals who can make this happen.
We have helped more than 30+ clients so far who are in need of Big Data solutions and delivered the results exactly the same. We also ensure that our solutions can boost your performance with Hadoop clusters for higher availability.
A highly-reliable SystemReliability is the thing which you can perfectly expect from us without any doubts. As we rightly blend data security and features together, W2S Solutions, the leading Data Engineering consulting company makes sure your Data is secured and accurate results are produced.
We also follow guidelines before implementation and ensure they are rightly followed in our development process.
Interactive, visual dashboardsWe don’t just stop with delivering Data Engineering Services rather we also make sure the dashboard which we develop are loaded with intuitive visuals and appealing features which make you and your team access frequently.
It comes with top-edge features such as auto-refreshing of data in real-time to make future decisions based on them without any delays. Moreover, it doesn’t involve any manual process and hence there are no possibilities of errors.
When it comes to Fintech, we make security our priority and help organizations in leveraging the entire loads of data for their business operations and eliminate or lower risks in the industry and also automate customer service processes.
Your industry can lively view the future data insights clearly to create upcoming discounts and offers based on them so that they can gear up their present business to a greater extent without any doubts in the industry.
As Agriculture is the keen part of every country, W2S Solutions focuses on offering top-notch Data Engineering Solutions. With the gathered data, predictive analysis can be done to yield a higher amount of data with the long-term health of farms.
In the Automotive industry, we provide outcomes that help businesses to process large quantities of data and implement Data Analytics strategies finely in order to create robust information-driven products for vehicles.
We are here to sort out the complexities in this sector and assist organizations in utilizing data assets to optimize and enhance the performance of the buildings as well as predict the future of the industry including power shortage or other issues.
With our unique solutions, industries can integrate data from client calls along with its records, network equipment, server logs, and other sources for better customer retention services with enhanced networks all over the world.
The transportation industry can now easily blend real-time and historical data which is implemented perfectly with next-generation algorithms in order to rule out the present challenges such as traffic, optimizing routes, and analyzing the future accordingly.
You as a client can easily analyze data sets generated by smart meters and grid equipment to eliminate costs and improve efficiency by managing energy consumption needs to the end customers globally.
The Main objective of this process is to automate the data which is available in data lake and ensure the high-quality analytical data from various sources.
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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.
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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.
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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.
Know More
The Main objective of this process is to automate the data which is available in data 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 data 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.
In order to make sure data is safe and available anytime, we back up all types of data with all users at night with encryption measures for those files. We also make sure servers are updated with the latest security factors and can seamlessly with a network protected using measures such as firewalls, intrusion-detection systems, etc.
We deal with data from every business irrespective of how complex they are. W2S Solutions keeps up with the trends in the industry and implement premier tools and safety precautions in handling data for your business. We always figure out a way to make your requirements a reality.
Yes, we have! As we deal with the current trends and next-gen tools and technologies, we have a unique infrastructure that every client expects. With us, you can leverage cost-effective software implementation easily.
We usually deal with a couple of models involved in Data Engineering Technology which include Predictive and Descriptive. As the name suggests, Predicitive models are here to describe what would happen in the future and why it may!
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+91 73387 73388
+971 55 818 7507
+1 737 217 3481
+1 647 493 7374