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Data Engineering Services

With our data engineering consulting services, we have been helping organizations prepare for, and deliver during disruptions.

What is data engineering

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.

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#W2SSOLUTIONS

Becoming a data-driven enterprise

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 Inefficient and outdated data strategies and infrastructures will further squeeze the company’s inability to leverage data, making it difficult to stay ahead of the curve.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.

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Let’s talk facts

Benefits of Data Engineering Services

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Better and Faster Decisions

We have helped our global clients optimize their data infrastructures to make faster and better decisions for their organizations.

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Getting Ready for AI

Our data engineering solutions are helping our clients adapt to the AI disruptions taking over their industry.

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Fostering Innovations and Cutting Costs

Our data strategies have helped our clients reimagine their organizational design, enabling them to execute high-value projects at scale.

Capabilities

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 data engineering 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 data engineering 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.

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PROFICIENCY

Our Data Engineering Tools and Technologies

Our data engineers are primarily problem solvers. They are highly proficient across different tools and technologies, which makes them the best data engineering consultants in the market. Their problem-solving capabilities amplify when they use such state-of-the-art tools and tech.

Data

Data Engineering

  • imgRedis
  • imgPostgreSQL
  • imgHive
  • imgCassandra
  • imgSolr
  • imgCosmos DB
  • imgNoSQL
  • imgHadoop

Data Science

  • imgPython
  • imgRapidMiner
  • imgScala
  • imgR
  • imgStata
  • imgApache Kafka
  • imgMongoDB
  • imgApache Storm

Data Visualization

  • imgJava
  • imgMySQL
  • imgMSSQL
  • imgTableau
  • imgPower BI
  • imgAzure
  • imgSnowflake
  • imgFusionCharts
  • imgPentaho
  • imgOracle
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Our Sectors

Industries We Serve

With more than 20+ years of expertise in data engineering services, we have assisted multiple industries in leveraging data for their businesses. Which eventually made a data revolution for them in boosting operations, acquiring valuable insights, and making informed decisions. These are a few major industries in which we have worked in our journey.
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FinTech

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Retail

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Agriculture

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Automotive

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Real Estate

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Telecom

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Transportation

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Energy

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Education

our history

Our Clients

Process

Learn about our Data Engineering process

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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

  • Great Expectations
  • Azure Databricks
  • Azure Blob storage
  • Azure Data lake

Data Quality Analyzing Process

  • Collecting sampled dataset from a data table using spark in the Azure data bricks.
  • Mounting the Blob Storage path of the Great Expectation folder into the Azure data bricks.
  • 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 Power BI to visualize the data.
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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

  • 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

Read More Read Less Read More
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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

  • 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

Read More Read Less Read More
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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

  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

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.

Read More Read Less Read More
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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.

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NDA

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Secured Policies for Devices & Role based access permission

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Stringent Security Measures

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they say

Testimonials

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anbarasan

Meet our data engineering consultant

Raman Narayanan
Software Architect

FAQ

FAQs About Data Engineering Services

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. You can also check out our blog on Data Analytics vs Big Data vs Data Science to know more about it.

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.

What is an efficient data management strategy?

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. 

What is the next big thing in data engineering?

  • Organizations Fueled by Data Insights.
  • Flexibility.
  • Optimized Productivity.
  • Increased Income.
  • Informed Decision-Making.
  • Responses Aligned with Patterns.
  • Optimal Approaches for the Team.
  • Spotting Potential Avenues.

What do you mean by data integration?

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.

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Explore Our Insights on Data Engineering

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INSIGHTS

Big Data vs Data Warehouse

Gain insights into the key differences between Big Data and Data Warehouses and their unique strategies.