How Boltic Helped Fynd to Shift their Data from MongoDB to BigQuery Without any Hassle
In today's data-driven world, organisations are constantly looking for ways to improve data accessibility, consistency, and integrity, to make informed and effective decisions. One such organisation, Fynd, a leading e-commerce platform in India, faced challenges in consolidating and analysing data from multiple sources, including their MongoDB database. To address this, Fynd leveraged Boltic.io's capabilities to transfer data from MongoDB to BigQuery, resulting in centralised data storage, real-time data analysis, compliance and reporting, and more. In this case study, we will explore how Boltic.io helped Fynd overcome its data challenges and the benefits it experienced.
Some important use cases for Data Warehousing using Boltic
1. Centralised Data Storage:
Boltic enables organisations to consolidate their data from various sources, such as different databases, applications, and systems, into a single data warehouse in BigQuery. This data centralization allows for a unified view of data, making it easier for organisations to access, manage, and analyse their data. With Boltic.io, organisations can easily integrate their data sources using automated and customizable data mapping, cleansing, and validation. It ensures that data is transformed and loaded into BigQuery in a consistent and structured format, making it easier to query and analyse.
Moreover, having a centralised data storage solution can also improve data security, as data can be stored in a single location with access controls and permissions set accordingly. It allows organisations to manage better and secure their data, reducing the risk of data breaches and unauthorised access. Some of the benefits of using Boltic.io for centralised data storage include improved data accessibility, data consistency, and data security. Organisations can also save time and resources by using Boltic.io's automated data integration capabilities, which reduces the need for manual data integration tasks.
2. Advanced-Data Analytics:
One of the significant advantages of using Boltic.io for data warehousing is the ability to perform advanced data analytics on the consolidated data in BigQuery. With data from various sources being integrated into a single location, businesses can comprehensively analyse the data, perform complex queries, and generate meaningful insights. By leveraging big query's powerful data analytics capabilities, organisations can gain deeper insights into various aspects of their business, such as customer behaviour, product performance, sales trends, and more. These insights can drive data-driven decision-making and improve overall business performance.
For instance, businesses can use data analytics to identify patterns and trends in customer behaviour, such as buying habits or product preferences. By analysing this data, businesses can improve their marketing and sales strategies, better target their audience, and optimise their product offerings. Data analytics can also identify inefficiencies in business operations and processes, such as supply chain management or inventory control. Businesses can optimise their operations, reduce costs, and improve overall efficiency by analysing this data.
3. Real-time Data Processing:
Real-time data processing refers to the ability to process and analyse data in real time as it is being generated or collected. With Boltic.io, organisations can stream data from different sources, including MongoDB, and load it directly into BigQuery in real time. It allows for immediate analysis and insights, which can be especially useful for applications such as fraud detection, real-time monitoring of customer behaviour, or tracking performance metrics. For example, an e-commerce company could use Boltic.io to stream data from their online store's transactional database into BigQuery in real time. It would allow them to monitor customer behaviour, identify trends and patterns, and provide real-time personalised recommendations and promotions.
A financial services company could use Boltic.io to stream data from their transactional database to BigQuery to monitor transactions in real-time for fraudulent activity, enabling them to respond quickly to potential threats. By using Boltic.io to enable real-time data processing, organisations can improve their decision-making processes by quickly responding to changes and identifying trends as they happen. It can improve operational efficiency, enhance customer experience, and increase revenue opportunities.
4. Historical Data Analysis:
Historical data analysis is a critical aspect of data warehousing that can help organisations identify patterns and trends over time and make informed decisions based on past performance. Boltic.io can transfer historical data from MongoDB to BigQuery, allowing organisations to analyse and gain insights from historical and real-time data. By integrating historical data into BigQuery, organisations can perform data analysis on large data sets and identify patterns that would otherwise go unnoticed. They can use this information to optimise business processes, improve product offerings, and gain a competitive advantage in their respective markets.
For example, a retail company can use historical data to analyse customer behaviour and trends, such as purchasing patterns, product preferences, and seasonality. This information can help the company optimise inventory management, pricing strategies, and marketing campaigns, ultimately increasing revenue and customer satisfaction. Historical data can be used for predictive modelling to forecast future trends, patterns, and outcomes. By analysing historical data alongside real-time data, organisations can identify patterns and trends that can help them make more accurate predictions about future events. It can help organisations make more informed decisions about resource allocation, risk management, and growth strategies.
5. Product Audit and Compliance:
Product audit and compliance are critical for many organisations, particularly those in regulated industries such as finance, healthcare, and pharmaceuticals. By leveraging Boltic.io's data integration platform, organisations can ensure that product audit data from MongoDB is seamlessly transferred to BigQuery for analysis and compliance purposes. With Boltic.io, organisations can automate transferring product audit data from MongoDB to BigQuery. It includes data mapping, cleansing, and validation to ensure data accuracy, consistency, and integrity. Once the product audit data is loaded into BigQuery, organisations can use BigQuery's powerful data analytics capabilities to perform advanced data analysis and generate meaningful insights.
Product audit data can provide valuable information about user behaviour, product performance, and compliance with regulatory requirements. By analysing product audit data in BigQuery, organisations can identify potential issues or anomalies, track changes in product behaviour over time, and ensure compliance with regulatory requirements. Boltic can also create visualisations, dashboards, and reports based on the analysed product audit data in BigQuery. These reports can be used by various stakeholders across the organisation, including product managers, marketing teams, and compliance officers, to make data-driven decisions, track product performance, and ensure compliance with regulatory requirements.
6. Data Integration and Transformation:
Data integration and transformation are crucial steps in the data warehousing process. Boltic.io's data integration platform enables organisations to seamlessly extract data from different sources and transform it into a format quickly loaded into BigQuery. Data mapping involves mapping the data from the source database to the schema of the target database. Boltic.io can automatically map the data between MongoDB and BigQuery, saving time and effort. Data cleansing involves removing any inconsistencies or errors from the data. Boltic.io can perform data cleansing tasks such as removing duplicates, correcting formatting issues, and standardising data values.
Data enrichment involves enhancing the data with additional information to provide context and insight. Boltic.io can enrich data by combining it with other sources or adding calculated fields. Data validation ensures that the data is accurate, consistent, and reliable. Boltic.io can perform data validation tasks such as checking for data integrity issues, ensuring referential integrity, and verifying that data meets business rules and requirements. By using Boltic.io for data integration and transformation, organisations can ensure that their data is accurate, consistent, and reliable, essential for making informed business decisions.
7. Business Intelligence and Reporting:
Business Intelligence (BI) and reporting are essential for data-driven decision-making. Organisations can use BI tools to create dashboards, reports, and visualisations based on the data stored in BigQuery. These tools can help stakeholders across the organisation to gain insights into key performance indicators, track progress toward goals, and identify areas for improvement. With Boltic.io, organisations can quickly and easily integrate data from multiple sources, transform it into a format compatible with BigQuery, and analyse it in real-time. This can enable stakeholders to gain insights into their business operations, customer behaviour, and market trends, which can help them to make data-driven decisions.
How did Boltic help Fynd to unlock the full potential of its Data?
Once upon a time in the bustling city of Mumbai, India, a group of young, energetic entrepreneurs founded an e-commerce company called Fynd. Fynd was destined to become a game-changer in the Indian online marketplace, offering a wide variety of products from fashion and electronics to home décor. The founders recognized the potential of technology in enhancing the user experience and streamlining operations, and thus, they built Fynd on a strong technological foundation. Their secret weapon was MongoDB, a NoSQL database that allowed them to collect and store massive amounts of product audit data. With this data, they hoped to gain insights into user behaviour, product performance, and regulatory compliance.
As Fynd rapidly gained popularity, they set their sights on optimising the supply chain and inventory management. But they soon discovered that MongoDB was not well-suited for complex analytical queries. To unlock the full potential of their data, Fynd's founders decided to seek the help of Boltic, a cloud-based data integration and transformation platform. Boltic specialised in transferring data from multiple sources to BigQuery, Google Cloud's powerful data warehousing solution. The partnership between Fynd and Boltic marked the beginning of a remarkable journey that would transform Fynd's data analytics capabilities and ultimately, its success in the e-commerce landscape.
The integration process began with Boltic's ETL (Extract, Transform, Load) process, which connected to Fynd's MongoDB database and extracted product audit data. This data was then transformed to match bigquery's requirements and loaded into the new data warehouse. With the data in BigQuery, Fynd could now use its powerful analytics features to gain valuable insights. As Fynd delved into the world of data analysis, they began to uncover fascinating trends and patterns. They could now identify popular products and categories, track inventory levels in real time, and understand how customers interacted with their website. This newfound knowledge allowed Fynd to make data-driven decisions, ultimately improving customer satisfaction and driving business growth.
In addition to the improvements in data analysis, Fynd also experienced significant cost optimization. BigQuery's pay-as-you-go pricing model meant that Fynd only paid for the resources they used, saving them money on storage and processing costs. This financial benefit allowed Fynd to invest more resources in other critical areas of the business. The partnership with Boltic also increased Fynd's overall productivity. With the automated data integration platform, Fynd's IT team no longer needed to manually extract and transform data from MongoDB. This freed up their time to focus on other important tasks, such as developing new features and enhancing the user experience.
As Fynd continued to grow, they used boltic's reporting capabilities to create visualisations, dashboards, and reports based on the analysed product audit data. These reports provided valuable insights for various stakeholders within the organisation, including product managers, marketing teams, and compliance officers. By having access to this data, Fynd was able to make informed decisions, track product performance, and ensure compliance with regulatory requirements. The story of Fynd and Boltic is a testament to the transformative power of data and technology. By harnessing the potential of BigQuery and Boltic's data integration platform, Fynd was able to unlock valuable insights, optimise costs, and improve decision-making.
This powerful alliance enabled Fynd to become a major player in the Indian e-commerce market, delivering a seamless and hassle-free shopping experience to millions of customers across the country. And so, with a strong foundation in data analytics and a commitment to continuous improvement, Fynd continued to thrive and expand, carving out a bright future for itself in the ever-evolving world of e-commerce. And it is all possible because of boltic’s power. With each passing day, Fynd's success story grew, serving as an inspiration for businesses everywhere, proving that with the right tools, technology, and determination, anything is possible.
Detailed Overview of the above case study
Note: Fynd is the parent company of Boltic.
Fynd is an Indian e-commerce company that operates an online marketplace for various products, including fashion, electronics, and home decor. It has quickly gained popularity in the Indian market for its ability to provide customers with a seamless and hassle-free shopping experience. One of the critical aspects of its business model is its focus on using technology to streamline its operations and deliver a better user experience. This is evident from the company's use of MongoDB, a NoSQL database, to collect and store a large volume of data related to product audits. The product audit data collected by Fynd includes information related to user actions, changes, and transactions. By analysing this data, Fynd wants to gain valuable insights into user behaviour, track changes in product performance, and ensure compliance with regulatory requirements.
For example, Fynd wants to use this data to identify popular products and categories, understand how customers interact with the website, and track inventory levels in real time. In addition to improving the user experience, Fynd also wants to use data analytics to optimise its supply chain and inventory management. They want to identify slow-moving products by analysing product audit data and adjusting their inventory levels accordingly. It helps the company to reduce inventory holding costs and improve its profitability. That's when Fynd turned to Boltic, a cloud-based data integration and transformation platform that helps organisations transfer data from multiple sources to BigQuery for advanced analytics and reporting. They decided to use Boltic.io's data integration platform to transfer their product audit data from MongoDB to BigQuery.
MongoDB is a popular NoSQL database that Fynd uses to collect a large volume of data related to product audits. MongoDB is not ideal for running complex analytical queries and generating insights from the data. Therefore, Fynd decided to move its product audit data to a more robust data warehousing solution, BigQuery, offered by Google Cloud. Transferring data from one database to another can be complex and time-consuming, mainly when dealing with large volumes of data. Boltic.io's data integration platform provides a seamless and automated way to transfer its product audit data from MongoDB to BigQuery. The platform includes several features that make the data transfer process smooth and efficient.
Features that make Data Transfer Process Smooth
1. Data Mapping:
Firstly, Boltic.io's platform provides data mapping, which defines how data from one source should be transformed to fit into another data structure. In this case, Boltic.io's platform maps the product audit data from MongoDB to the format required by BigQuery. It is an essential step to ensure the data is transferred correctly and can be queried efficiently in the new data warehouse.
2. Data Cleansing:
Secondly, Boltic.io's platform includes data cleansing, which identifies and corrects inaccurate or inconsistent data. It is essential when transferring data from one database to another, as data inconsistencies can cause errors in the analysis and interpretation of the data. Boltic.io's platform automatically detects and corrects data inconsistencies, ensuring that the data transferred to BigQuery is accurate and consistent.
3. Data Validation:
Finally, Boltic.io's platform includes data validation, which is the process of verifying that the data transferred to BigQuery is complete and error-free. This is an essential step to ensure that the data is used for analysis and can be trusted by the users. Boltic.io's platform validates the data transferred from MongoDB to BigQuery, ensuring it is complete and error-free.
Boltic.io's data integration platform provides several benefits for Fynd. The platform enables them to transfer its product audit data from MongoDB to BigQuery efficiently and accurately without manual intervention. It saves time and reduces the risk of errors in the data transfer process. Using BigQuery as the data warehousing solution allows them to analyse its product audit data more effectively, gaining insights into user behaviour, tracking changes in product performance, and ensuring compliance with regulatory requirements. Boltic.io's data integration platform was used to transfer product audit data from MongoDB to BigQuery, Google Cloud's data warehousing solution.
How Fynd Followed Steps to Implement Boltic
1. Data Extraction
Boltic.io's ETL process connects to Fynd's MongoDB database and extracts product audit data, including user actions, changes, and transactions. The extraction process is automated and collects data at regular intervals to keep the warehouse updated with the latest data.
2. Data Transformation
Boltic.io transforms the extracted data into a format compatible with BigQuery's requirements. This includes data mapping to match the data structure in BigQuery, data cleansing to remove any inconsistencies or errors, and data validation to ensure data integrity. The platform provides a range of data transformation features to enable data cleansing, filtering, and data type conversion.
3. Data Loading
Boltic.io loads the transformed product audit data into BigQuery, which creates a centralised data warehouse for storing and analysing the data. The platform uses BigQuery's APIs to load data in a batch or streaming mode.
4. Data Analysis
Once the product audit data is loaded into BigQuery, Fynd uses big query's powerful data analytics capabilities to analyse the data. They run complex queries, perform advanced data analysis, and generate meaningful insights into user behaviour, product performance, and compliance with regulatory requirements. BigQuery is a fully-managed, cloud-native data warehouse that enables organisations to analyse large datasets quickly and easily. It offers a range of features for data analysis, including real-time analytics, machine learning, and data visualisation.
5. Reporting and Compliance
Fynd uses business intelligence tools to create visualisations, dashboards, and reports based on the analysed product audit data in BigQuery.
These reports are used by various stakeholders across the organisation, including product managers, marketing teams, and compliance officers, to make data-driven decisions, track product performance, and ensure compliance with regulatory requirements. The platform offers a range of features for data visualisation, including charting, drill-downs, and filtering. Using Boltic.io's data integration platform to transfer product audit data from MongoDB to BigQuery, Fynd achieved several significant results, including centralised data storage, real-time data analysis, and compliance and reporting capabilities.
How Fynd Getting Results by using Boltic’s Integration Platform
1. Centralised Data Storage:
The centralised data storage in BigQuery enables Fynd to store all product audit data in a structured and organised manner. With a centralised data warehouse, the company can access the data easily, ensuring consistency and data integrity. This data centralization also enables the company to reduce duplication and avoid data silos that can lead to inconsistencies and inaccuracies in data analysis.
2. Real-time Data Analysis:
One of the most significant benefits of using Boltic.io and BigQuery is the ability to perform real-time data analysis. Fynd can track user behaviour, identify product issues, and take timely actions to improve customer experience. Real-time data analysis enables the company to react quickly to market changes and customer needs, improving overall customer satisfaction and driving business growth.
3. Compliance and Reporting:
Fynd also uses the analysed product audit data in BigQuery to ensure compliance with regulatory requirements. Compliance is a critical concern for e-commerce companies, and Fynd can generate reports to meet both internal and external regulatory compliance requirements. The company can also track changes in product behaviour, identify issues related to regulatory compliance, and take proactive measures to address them.
4. Advance Reporting:
The reporting capabilities in BigQuery allow Fynd to create visualisations, dashboards, and reports based on the analysed product audit data. These reports provide insights into user behaviour, product performance, and compliance with regulatory requirements. Various organisational stakeholders, including product managers, marketing teams, and compliance officers, use these reports to make data-driven decisions, track product performance, and ensure compliance with regulatory requirements.
5. Improved Decision-making:
With the ability to access and analyse product audit data in real time, Fynd's decision-making process has improved significantly. They can quickly identify trends, patterns, and anomalies and make data-driven decisions aligning with their business goals.
6. Cost Optimization:
By moving its data from MongoDB to BigQuery, Fynd was able to optimise its costs significantly. BigQuery offers a pay-as-you-go pricing model, which means Fynd only pays for the resources they use. It has helped them save money on storage and processing costs.
7. Increased Productivity:
With Boltic.io's automated data integration platform, Fynd's IT team no longer needs to manually extract and transform data from MongoDB. This has freed up their time, allowing them to focus on other critical tasks, such as developing new features, improving user experience, and enhancing the overall performance of their platform.
Using Boltic.io's data integration platform and BigQuery, Fynd significantly improved data management, analysis, compliance, and reporting. These improvements have helped the company to improve customer experience, drive business growth, and meet regulatory requirements.
Fynd's partnership with Boltic.io to transfer their product audit data from MongoDB to BigQuery was a strategic decision that helped them achieve significant benefits. By centralising their data in a structured and organised manner in BigQuery, they gained a holistic view of their product audit data, enabling them to make informed decisions based on accurate insights. The real-time data processing capabilities provided by Boltic.io allowed Fynd to analyse their product audit data in real-time, enabling them to track user behaviour, identify product issues, and take timely actions to improve customer experience.
By using the analysed data to ensure compliance with regulatory requirements and generate reports for stakeholders, Fynd maintained transparency and accountability within their organisation. This collaboration helped Fynd enhance its business operations, improve customer satisfaction, and increase its competitive advantage in the e-commerce industry. The case study is an excellent example of how businesses can leverage data integration platforms to streamline their data management processes, gain valuable insights, and make data-driven decisions.
What is Boltic.io and how does it help move data from MongoDB to BigQuery?
What are the benefits of using Boltic.io for moving data from MongoDB to BigQuery?
Can Boltic.io handle large amounts of data when moving from MongoDB to BigQuery?
Is any technical expertise required to use Boltic.io for data migration from MongoDB to BigQuery?
Does Boltic.io offer any data validation or error checking during the migration process?
What other data sources can Boltic.io work with for data migration besides MongoDB and BigQuery?