W2S Solutions 
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Outperform with technology 
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W2X LABS 
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Contact us
W2S Solutions -----
Outperform with technology -----
W2X LABS
In simple words, data engineering is the foundational process that enables the effective management and utilization of data in an organization. It sets the base for analysts, leaders, and your team to analyze the data at hand, uncover crucial insights, and evolve into a data-driven organization. Our data engineering consultants also help businesses design, strategize, develop, and manage their data infrastructures, while also helping them to collect, store, and process large volumes of data.
#W2SSOLUTIONS
As digital powers almost every business and consumer interaction in the market, companies are overwhelmed by the humongous volumes and the chaotic nature of data created every day. Inefficient and outdated data strategies and infrastructures will further squeeze the company’s inability to leverage data, making it difficult to stay ahead of the curve.
We help our clients solve these challenges by taking a modern approach that puts the focus on the holistic growth of the organization. Our data engineers will collaborate with you to identify the key objectives of your digital and business strategies. This will allow us to design and build data infrastructures that align with your goals and operations.
Our data engineering solutions and consulting services have helped businesses across the globe to evolve into data-driven enterprises. For more than two decades, our data engineers have been shaping industries and market trends by building robust, easily accessible, and secure data infrastructures.
We strongly believe that being data-driven can unlock a company’s full potential. So, we partner with the leading technology providers and platforms to help you achieve that. With the oncoming AI wave, data will be the top priority for companies moving forward. As our approach puts data at the very DNA of your organization, we can help you harness this and other innovations that will future-proof your organization.
Let’s talk facts
We have helped our global clients optimize their data infrastructures to make faster and better decisions for their organizations.
Our data engineering solutions are helping our clients adapt to the AI disruptions taking over their industry.
Our data strategies have helped our clients reimagine their organizational design, enabling them to execute high-value projects at scale.
Capabilities
Data lake implementation allows you to store and process high-volume data at minimal resource spending. Our solutions expand your organization to construct a dynamic data storage systems.
Data architecture have the need to be highly scalable and accessible. Our cloud data architecture brings extreme simplification and minimalism to the table.
A data model that revolves around your core business model and vision can amplify your decision-making process and fetch greater ROI.
By helping enterprises visualize complex data, we make data-driven decision-making a part of your business process. Our data engineering solutions simplify multidimensional data exploration, allowing you to work with microscopic precision and context.
Data management without data integration leads to information stagnation. Our data engineering solutions integrate data from diverse resources and make it accessible for the entire enterprise to rekindle the spirit of data-inspired operations.
We help businesses leverage influential technologies to convert raw data into powerful insights, ultimately fetching higher ROI and enhancing decision-making.
our history
Process
OUR CASE SPEAKS
Client Success
Data Quality Management
Objective
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 a.......
Objective
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.
Solution Architecture
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.
Tech-Stack
Data Quality Analyzing Process
Azure synapse ETL with DW
Introduction:
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.......
Introduction:
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.
Raw data ingestion design
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.
Step 1:
Pipeline reads data from config file to get database, tables, path
Step 2:
Using ADF linked service and data set objects, copy data from source to sink
Step 3:
All raw data ingestion load is configured to perform “Truncate and load”
Azure Synapse destination:
Pipeline auto-creates tables directly based on source column names and data types
Data transformation design
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
SQL script driven
Pipeline reads data from config file to get database, tables, path
Visual way of transformation – Code free
Using ADF Data Flow Activity to transform & load data into Synapse
Transformation using T-SQL
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
Azure synapse ETL with snowflake
Introduction:
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.......
Introduction:
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.
Raw data ingestion design
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.
Step 1:
Pipeline reads data from config file to get database, tables, path
Step 2:
Using ADF linked service and data set objects, copy data from source to sink
Step 3:
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 design
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
SQL script driven
Pipeline reads data from config file to get database, tables, path
Visual way of transformation – Code free
Using ADF Data Flow Activity to transform & load data into Synapse
Transformation using T-SQL
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
Azure synapse -Fintech
Introduction:
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.......
Introduction:
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.
Ingestion RAW data
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
Transformation of RAW data
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
Power BI representation:
Setting up the power BI tools and connecting with synapse for designing the reports based on business requirements.
Leveraging data can help you go from surviving to excelling.
Let's make the world data positive!
We take data protection seriously. Way too seriously!
Your data is safe with us. We have a strong moral compass and complete transparency to maintain the bond between us. Furthermore, we follow the best industry practices to keep your data safe and tight.
NDA
Secured Policies for Devices & Role based access permission
Stringent Security Measures
they say
The professional quality mobile experience developed by W2S Solutions for our customers is a valued enhancement to our product offering. Working with their team through several iterations was a positive experience that produced excellent results.
Daryl Sedgman
CISO, Founder at Fiix Software
We handpicked W2S Solutions for their straightforward vision of sustainability. Their ability to use technology to solve a real-time problem is quite impressive! Integrating IT Solutions with firmware devices and offering time-critical solutions with zero deficiency which saves humanity is not an easy thing to do. W2S Solutions has achieved it easily and wisely.
Atmanand
Director of NIOT
We strongly believe that technology can simplify our vision for sustainability and businesses can create a solid impact on the society by being inclusive of such a vision. Leveraging the rapid technological innovation, we help government agencies and large enterprises with their vision for sustainability, allowing them to optimize their entire infrastructure for better results.
Tyler Shandro
Entrepreneur
W2S solutions have been great to work with - they have been incredible listeners and have been very approachable for my every need. They have allowed such a stressful process to be an easy journey and helped develop my dream project. I will be forever grateful for the work and effort they have put in as a team, and I would recommend them in a heartbeat.
Lourdes Mariathas
Founder of Squeaki Ltd
FAQ
Data engineering is a growing field that allows big tech companies to leverage data in order to create value. By using data engineering services, companies can access and process large datasets quickly, accurately, and securely. For better decision-making, they can also use advanced analytics and machine learning.
Data engineering consultants are responsible for developing and maintaining databases, designing architectures for storing big data, and optimizing query performance. They also develop ETL (Extract Transform Load) pipelines to move data between various sources. On the other hand, a data scientist's job is to collect, cleanse and analyze datasets using machine learning techniques such as neural networks or support vector machines in order to uncover patterns or trends that can be used to make more informed decisions. You can also check out our blog on Data Analytics vs Big Data vs Data Science to know more about it.
The demand for data engineers is high these days. In this role, they design and implement data pipelines, architectures, and systems with an emphasis on efficiency. They create databases and ETL (extract, transform, load) processes that allow companies to access, analyze and visualize data. Data engineering companies provide specialized services to help businesses deal with ever-increasing amounts of data.
Data engineering is an essential part of the modern business world, and its importance will only grow as more companies rely on data-driven decision making. The future of data engineering will be shaped by advancements in technology and the increasing demand for data-driven insights.
Data engineering services can help companies to cope with the challenges of managing and interpreting data. These services are increasingly becoming popular among organizations as they can provide the necessary insights to make informed decisions.
Companies that need to analyze large amounts of data from multiple sources can benefit most from data engineering solutions. They can help organizations aggregate, store, and process data in a way that makes it easier to access and analyze. Additionally, these services allow businesses to build custom applications that help them better understand their data and make informed decisions.
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!
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 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.
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.
An efficient data management strategy is a meticulous plan that encompasses the acquisition, storage, organization, and utilization of data to derive maximum value. It involves implementing streamlined processes for data collection, ensuring accuracy, relevance, and compliance with regulations.
Data integration refers to the process of bringing together data from multiple sources across an organization to provide a complete, accurate, and up-to-date dataset for BI, data analysis and other applications and business processes.
INSIGHTS
Gain insights into the key differences between Big Data and Data Warehouses and their unique strategies.
INSIGHTS
Master the ETL process for seamless data integration with expert strategies. Discover tips and start mastering ETL today.
INSIGHTS
Data Engineering Accelerators & More
we have discussed the various factors to consider in Data Operations since Data Engineering is the backbone of every business.
Rattha Tek Meadows, Sholinganallur, Chennai, India
691 S Milpitas Blvd Ste 217,
Milpitas CA 95035, USA.
9820, Ivalenes Hope Drive,
Austin, TX, USA.
3D, Gold Tower, Cluster I,
Dubai.
226 Buttercup, Waterloo, ON N2V 2WB, Canada.
Redis
PostgreSQL
Hive
Cassandra
Solr
Cosmos DB
NoSQL
Hadoop
Python
RapidMiner
Scala
R
Stata
Apache Kafka
MongoDB
Apache Storm
Java
MySQL
MSSQL
Tableau
Power BI
Azure
Snowflake
FusionCharts
Pentaho
Oracle
