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$30 USD / hora
Bandera de INDIA
trivandrum, india
$30 USD / hora
Aquí son las 9:42 a. m.
Se unió el septiembre 28, 2019
0 recomendaciones

Sreejith B.

@amwazeindia

 5,0 (27 comentarios)
5,5
5,5
96%
96%
$30 USD / hora
Bandera de INDIA
trivandrum, india
$30 USD / hora
100 %
Trabajos finalizados
100 %
Dentro del presupuesto
99 %
A tiempo
14 %
Tasa de recontratación

Microsoft Certified | Power BI | Azure

I specialize in creating customized solutions for data processing, reporting and visualising using Power BI, Excel, Google Sheets and Looker Studio (Google Data Studio). Well adept in Python, DAX, M, SQL and VBA, I can also help you with process flow automation using Power Automate and Logic App. I am a Microsoft Certified Data Analyst Associate and currently preparing for the next certification on Data Engineering using Azure. Coming to my background, I have a degree in Mechanical Engineering and worked as a marine engineer for 10 years. Afterwards, I have been working as a freelance consultant for Power BI projects since 2019. Have helped organizations establish a Power BI and Azure architecture ( ADLS2, ADF , Azure SQL and Dataflows) from the scratch, train and build a team of Power BI developers and provide back end support for analytics solutions.Currently working as a Power BI consultant for a telecom infrastructure development company in EU.
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Contacta Sreejith B. sobre tu trabajo

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Portafolio

11586929
Data engineering project in Azure. Raw data is from web and a csv received in a email inbox. Objective is to have the data made available for long term historical analysis and visualize it in Power BI Tools used : - Google Cloud APIs - Azure Data Lake Storage - Azure Data Factory - Azure SQL - Azure Synapse Analytics - Azure Databricks - Lake Data House (Fabric) - Python (pandas) - Azure Function App - Azure Logic App - Power BI
Data Engineering in Azure
Data engineering project in Azure. Raw data is from web and a csv received in a email inbox. Objective is to have the data made available for long term historical analysis and visualize it in Power BI Tools used : - Google Cloud APIs - Azure Data Lake Storage - Azure Data Factory - Azure SQL - Azure Synapse Analytics - Azure Databricks - Lake Data House (Fabric) - Python (pandas) - Azure Function App - Azure Logic App - Power BI
Data Engineering in Azure
Data engineering project in Azure. Raw data is from web and a csv received in a email inbox. Objective is to have the data made available for long term historical analysis and visualize it in Power BI Tools used : - Google Cloud APIs - Azure Data Lake Storage - Azure Data Factory - Azure SQL - Azure Synapse Analytics - Azure Databricks - Lake Data House (Fabric) - Python (pandas) - Azure Function App - Azure Logic App - Power BI
Data Engineering in Azure
11531363
Issue tracker dashboard using Power BI. Raw data is from a web api output as a json file. An Azure Logic App takes this data from the web service and saves the json in a Google Drive, which serves as a staging container. An Azure Data Factory pipeline takes this data from there. In the pipeline a Synapse notebook activity is added, which will carry out the ELT process. The cleaned data is stored in an Azure Data Lake Storage, in parquet format. Using Azure Synapse Analytics, a serverless Data Lake is created, which can be queried in the Power BI using SQL statements. Another part of the data is a csv file that is received as an email in gmail . An appscript is created in a google sheet, stored in the previously mentioned staging Google Drive, which will copy the contents of the csv file into the google sheet. From the Google Sheet, another Data Factory pipeline will take the data and store it in the serverless data lake in csv format.
Power BI Report
Issue tracker dashboard using Power BI. Raw data is from a web api output as a json file. An Azure Logic App takes this data from the web service and saves the json in a Google Drive, which serves as a staging container. An Azure Data Factory pipeline takes this data from there. In the pipeline a Synapse notebook activity is added, which will carry out the ELT process. The cleaned data is stored in an Azure Data Lake Storage, in parquet format. Using Azure Synapse Analytics, a serverless Data Lake is created, which can be queried in the Power BI using SQL statements. Another part of the data is a csv file that is received as an email in gmail . An appscript is created in a google sheet, stored in the previously mentioned staging Google Drive, which will copy the contents of the csv file into the google sheet. From the Google Sheet, another Data Factory pipeline will take the data and store it in the serverless data lake in csv format.
Power BI Report
Issue tracker dashboard using Power BI. Raw data is from a web api output as a json file. An Azure Logic App takes this data from the web service and saves the json in a Google Drive, which serves as a staging container. An Azure Data Factory pipeline takes this data from there. In the pipeline a Synapse notebook activity is added, which will carry out the ELT process. The cleaned data is stored in an Azure Data Lake Storage, in parquet format. Using Azure Synapse Analytics, a serverless Data Lake is created, which can be queried in the Power BI using SQL statements. Another part of the data is a csv file that is received as an email in gmail . An appscript is created in a google sheet, stored in the previously mentioned staging Google Drive, which will copy the contents of the csv file into the google sheet. From the Google Sheet, another Data Factory pipeline will take the data and store it in the serverless data lake in csv format.
Power BI Report
8946370
CoVid Dashboard using Plotly and Dash. Deisgn insipred by : https://dribbble.com/shots/14995855/attachments/6716678?mode=media Data used : https://dashboard.kerala.gov.in/ collected in a google sheet Analysis done using Pandas . Visualized using Plotly libraries in Python and dashboard created in Dash. Styling done using custom css This project is still in progress
Dashboard using Plotly and Dash
8623491
Dashboard designed as a part of contest. Though the contest was not won, I believe I was able to create a good looking functional dashboard. To have a clutter free experience, dimensions that are related added as optional metrics which can be easily switched between by a flick of a button. Multiple pages in the dashboards are linked via buttons and provides a seamless navigation. Drill throughs also provided to the charts offering more analytics. Time taken : ~3 hours
Google Data Studio Dashboard
Dashboard designed as a part of contest. Though the contest was not won, I believe I was able to create a good looking functional dashboard. To have a clutter free experience, dimensions that are related added as optional metrics which can be easily switched between by a flick of a button. Multiple pages in the dashboards are linked via buttons and provides a seamless navigation. Drill throughs also provided to the charts offering more analytics. Time taken : ~3 hours
Google Data Studio Dashboard
Dashboard designed as a part of contest. Though the contest was not won, I believe I was able to create a good looking functional dashboard. To have a clutter free experience, dimensions that are related added as optional metrics which can be easily switched between by a flick of a button. Multiple pages in the dashboards are linked via buttons and provides a seamless navigation. Drill throughs also provided to the charts offering more analytics. Time taken : ~3 hours
Google Data Studio Dashboard
8523489
Project : Analyze and visualize live traffic data of last 100 thousand vehicles in the roads of a city. Dataset : Google sheet with incoming data every 15 minutes Analysis and Visualization tool : Google Data Studio Time taken : 8 hours
Live Traffic Dashboard
Project : Analyze and visualize live traffic data of last 100 thousand vehicles in the roads of a city. Dataset : Google sheet with incoming data every 15 minutes Analysis and Visualization tool : Google Data Studio Time taken : 8 hours
Live Traffic Dashboard
Project : Analyze and visualize live traffic data of last 100 thousand vehicles in the roads of a city. Dataset : Google sheet with incoming data every 15 minutes Analysis and Visualization tool : Google Data Studio Time taken : 8 hours
Live Traffic Dashboard
8509511
Multiple teams job tracking sheet and a central dashboard in Excel. Data will be entered in separate sheets for each teams. All the data from multiple sheets are consolidated in a single sheet using query. Pivot tables are then created using this database and then visualized in the form of a dashboard.
Work tracker & dashboard using VBA and Excel
11586929
Data engineering project in Azure. Raw data is from web and a csv received in a email inbox. Objective is to have the data made available for long term historical analysis and visualize it in Power BI Tools used : - Google Cloud APIs - Azure Data Lake Storage - Azure Data Factory - Azure SQL - Azure Synapse Analytics - Azure Databricks - Lake Data House (Fabric) - Python (pandas) - Azure Function App - Azure Logic App - Power BI
Data Engineering in Azure
Data engineering project in Azure. Raw data is from web and a csv received in a email inbox. Objective is to have the data made available for long term historical analysis and visualize it in Power BI Tools used : - Google Cloud APIs - Azure Data Lake Storage - Azure Data Factory - Azure SQL - Azure Synapse Analytics - Azure Databricks - Lake Data House (Fabric) - Python (pandas) - Azure Function App - Azure Logic App - Power BI
Data Engineering in Azure
Data engineering project in Azure. Raw data is from web and a csv received in a email inbox. Objective is to have the data made available for long term historical analysis and visualize it in Power BI Tools used : - Google Cloud APIs - Azure Data Lake Storage - Azure Data Factory - Azure SQL - Azure Synapse Analytics - Azure Databricks - Lake Data House (Fabric) - Python (pandas) - Azure Function App - Azure Logic App - Power BI
Data Engineering in Azure
11531363
Issue tracker dashboard using Power BI. Raw data is from a web api output as a json file. An Azure Logic App takes this data from the web service and saves the json in a Google Drive, which serves as a staging container. An Azure Data Factory pipeline takes this data from there. In the pipeline a Synapse notebook activity is added, which will carry out the ELT process. The cleaned data is stored in an Azure Data Lake Storage, in parquet format. Using Azure Synapse Analytics, a serverless Data Lake is created, which can be queried in the Power BI using SQL statements. Another part of the data is a csv file that is received as an email in gmail . An appscript is created in a google sheet, stored in the previously mentioned staging Google Drive, which will copy the contents of the csv file into the google sheet. From the Google Sheet, another Data Factory pipeline will take the data and store it in the serverless data lake in csv format.
Power BI Report
Issue tracker dashboard using Power BI. Raw data is from a web api output as a json file. An Azure Logic App takes this data from the web service and saves the json in a Google Drive, which serves as a staging container. An Azure Data Factory pipeline takes this data from there. In the pipeline a Synapse notebook activity is added, which will carry out the ELT process. The cleaned data is stored in an Azure Data Lake Storage, in parquet format. Using Azure Synapse Analytics, a serverless Data Lake is created, which can be queried in the Power BI using SQL statements. Another part of the data is a csv file that is received as an email in gmail . An appscript is created in a google sheet, stored in the previously mentioned staging Google Drive, which will copy the contents of the csv file into the google sheet. From the Google Sheet, another Data Factory pipeline will take the data and store it in the serverless data lake in csv format.
Power BI Report
Issue tracker dashboard using Power BI. Raw data is from a web api output as a json file. An Azure Logic App takes this data from the web service and saves the json in a Google Drive, which serves as a staging container. An Azure Data Factory pipeline takes this data from there. In the pipeline a Synapse notebook activity is added, which will carry out the ELT process. The cleaned data is stored in an Azure Data Lake Storage, in parquet format. Using Azure Synapse Analytics, a serverless Data Lake is created, which can be queried in the Power BI using SQL statements. Another part of the data is a csv file that is received as an email in gmail . An appscript is created in a google sheet, stored in the previously mentioned staging Google Drive, which will copy the contents of the csv file into the google sheet. From the Google Sheet, another Data Factory pipeline will take the data and store it in the serverless data lake in csv format.
Power BI Report
8946370
CoVid Dashboard using Plotly and Dash. Deisgn insipred by : https://dribbble.com/shots/14995855/attachments/6716678?mode=media Data used : https://dashboard.kerala.gov.in/ collected in a google sheet Analysis done using Pandas . Visualized using Plotly libraries in Python and dashboard created in Dash. Styling done using custom css This project is still in progress
Dashboard using Plotly and Dash
8623491
Dashboard designed as a part of contest. Though the contest was not won, I believe I was able to create a good looking functional dashboard. To have a clutter free experience, dimensions that are related added as optional metrics which can be easily switched between by a flick of a button. Multiple pages in the dashboards are linked via buttons and provides a seamless navigation. Drill throughs also provided to the charts offering more analytics. Time taken : ~3 hours
Google Data Studio Dashboard
Dashboard designed as a part of contest. Though the contest was not won, I believe I was able to create a good looking functional dashboard. To have a clutter free experience, dimensions that are related added as optional metrics which can be easily switched between by a flick of a button. Multiple pages in the dashboards are linked via buttons and provides a seamless navigation. Drill throughs also provided to the charts offering more analytics. Time taken : ~3 hours
Google Data Studio Dashboard
Dashboard designed as a part of contest. Though the contest was not won, I believe I was able to create a good looking functional dashboard. To have a clutter free experience, dimensions that are related added as optional metrics which can be easily switched between by a flick of a button. Multiple pages in the dashboards are linked via buttons and provides a seamless navigation. Drill throughs also provided to the charts offering more analytics. Time taken : ~3 hours
Google Data Studio Dashboard
8523489
Project : Analyze and visualize live traffic data of last 100 thousand vehicles in the roads of a city. Dataset : Google sheet with incoming data every 15 minutes Analysis and Visualization tool : Google Data Studio Time taken : 8 hours
Live Traffic Dashboard
Project : Analyze and visualize live traffic data of last 100 thousand vehicles in the roads of a city. Dataset : Google sheet with incoming data every 15 minutes Analysis and Visualization tool : Google Data Studio Time taken : 8 hours
Live Traffic Dashboard
Project : Analyze and visualize live traffic data of last 100 thousand vehicles in the roads of a city. Dataset : Google sheet with incoming data every 15 minutes Analysis and Visualization tool : Google Data Studio Time taken : 8 hours
Live Traffic Dashboard
8509511
Multiple teams job tracking sheet and a central dashboard in Excel. Data will be entered in separate sheets for each teams. All the data from multiple sheets are consolidated in a single sheet using query. Pivot tables are then created using this database and then visualized in the form of a dashboard.
Work tracker & dashboard using VBA and Excel

Comentarios

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5,0
Precio no especificado
Automatic data validation in excel
Excellent job
Data Processing
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Excel
SQL
Database Administration
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Bandera de Satish K.
@satishkandikonda
•
hace 3 años
5,0
Precio no especificado
Project for Sreejith B. -- 2
With the kind of support and intuitive involvement that Sreejith does, I will be working with him over a long time. Highly Recommended Professional. In this Project we developed a VBA tool that reduced manual efforts to minimum and improved on accuracy as well.
Data Processing
Data Entry
Excel
Photoshop
+1 más
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Bandera de Abhay G.
@a020aga
•
hace 3 años
5,0
Precio no especificado
Project for Sreejith B.
Excellent work! On time, on budget!
Data Processing
Data Entry
Excel
Photoshop
+1 más
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Bandera de Srdjan D.
@sdrljaca
•
hace 3 años
5,0
Precio no especificado
Project Report in Power BI
100% dedicated to project and delivery
Power BI
Avatar del usuario
Bandera de Srdjan D.
@sdrljaca
•
hace 3 años
5,0
Precio no especificado
Project for Sreejith B.
Sreejith is an expert professional !! Understands the requirements and the project delivery was exactly as per expectations !! I am very satisfied and he is my go to man for any further needs. Looking ahead to many more collaborations !! Thanks Sreejith !!
Data Processing
Data Entry
Excel
Photoshop
+1 más
Avatar del usuario
Bandera de Abhay G.
@a020aga
•
hace 3 años

Educación

B-Tech Mechanical Engineering

University of Calicut, India 2002 - 2006
(4 años)

Calificaciones

Applied Skills : Create and manage automated processes by using Power Automate

Microsoft
2023
Create and manage automated processes by using Power Automate Nov 3, 2023

Microsoft Certified : Power BI Data Analyst Associate (PL300)

Microsoft
2023
Microsoft Power BI Data Analyst PL-300 Aug 8, 2023

SQL Bootcamp

Udemy
2020
Basics of SQL programming using POSTGRESQL The Complete SQL Bootcamp on Udemy https://www.udemy.com/course/the-complete-sql-bootcamp/

Contacta Sreejith B. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

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Principales habilidades

Excel 23
Data Processing 18
Google Sheets 8
Power BI 3
SQL 1

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