#W2SSOLUTIONS
An Overview Of Our Data Engineering Solutions
The 21st century is all about data. In an increasingly digitally aligned world, data makes all the difference in your business. Our data engineering services are specifically designed to help various startups and organizations to streamline their operations.
As a leading data engineering company in the market, we prepare businesses and organizations for a data-led transformation that brings their best foot forward in the market. A data-inspired approach can drastically change the impact your business or organization creates in the industry.
An efficient data management strategy can be a key factor that determines the success of your enterprise or organization. Right from internal optimization to R&D, data engineering solutions level the playground.
One of the most underrated benefits of a data-infused organization is that it lets your system evolve organically. As a result, it future-proofs your organization and empowers you to operate with maximum efficiency and 360-degree context.
Let’s talk facts
Benefits of Data Engineering Services
The science of decision making
We have helped many large-scale organizations to refine their decision-making process through data engineering solutions.
Data by 2025
An extensive market report suggests that we will be producing 175 zettabytes of data by 2025, and our data strategy can strengthen your arsenal!
To be future-proof
We devise our data strategy with influential tech like AI, ML, etc.
Offerings
Data Engineering services we perform
Data Lake Implementation
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.
Cloud Data Architecture
Data architecture have the need to be highly scalable and accessible. Our cloud data architecture brings extreme simplification and minimalism to the table.
Data Model Development
A data model that revolves around your core business model and vision can amplify your decision-making process and fetch greater ROI.
Data Visualization
By helping enterprises visualize complex data, we make data-driven decision-making a part of your business process. Our solutions simplify multidimensional data exploration, allowing you to work with microscopic precision and context.
Advanced Data Integration
Data management without data integration leads to information stagnation. Our solutions integrate data from diverse resources and make it accessible for the entire enterprise to rekindle the spirit of data-inspired operations.
Leveraging Big Data
We help businesses leverage influential technologies to convert raw data into powerful insights, ultimately fetching higher ROI and enhancing decision-making.
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
our history
Our Clients
Process
Learn about our Data Engineering process
OUR CASE SPEAKS
Case Studies
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 and manual proce.......
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
- Great Expectations
- Azure Databricks
- Azure Blob storage
- Azure Data lake
Data Quality Analyzing Process
- Collecting sampled dataset from data table using spark in the Azure databricks.
- Mounting the Blob Storage path of the Great Expectation folder into the Azure databricks.
- Preparing the batch of data to be evaluated.
- Executing the expectation validation on top of the selected sampled data.
- Collecting the Great Expectation results and store those results in a separate data lake table.
- Consuming the result data using the Power BI to visualize the data.
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 shows how Azur.......
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
- Surrogate key – MD5 Hash based on natural key
- MD5_HASH column – for all dimensional attributes to track the changes
- LAST_LOAD_TS - housekeeping column
- Dimensional attributes
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 activitie.......
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
- Surrogate key – MD5 Hash based on natural key
- MD5_HASH column – for all dimensional attributes to track the changes
- LAST_LOAD_TS - housekeeping column
- Dimensional attributes
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 o.......
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
- Ingest postgreSQL tables to Azure data lake storage gen2 and copy same data from ADLS gen2 to SQL dedicated pool
- 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.
- Ingest Flat files into Azure data lake storage gen2 and convert into external view to SQL dedicated pool
Transformation of RAW data
Dimension And Fact Load:
Step 1: Create SQL views for dimension that holds transformation logic
- Surrogate key – MD5 Hash based on natural key
- MD5_HASH column – for all dimensional attributes to track the changes
- LAST_LOAD_TS - housekeeping column
- Dimensional attributes
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
Testimonials
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
FAQ
FAQs of Data Engineering Solutions
How big tech companies use data engineering?
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.
What is the difference between Data Engineering and Data Science?
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.
What does a Data Engineer do?
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.
What is the future of Data Engineering?
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.
when does a company need a data engineering service?
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.
How predictive modeling is used across business functions?
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!
Do you have the adequate infrastructure and technology to support my business process?
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.
What problem does W2S Solutions solve?
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.
How do you keep our data safe?
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.
