Data Transformation


Turn raw data into business value

Easily transform your raw data into data models for analytics and machine learning use.
Features

Helping you elevate your data
management experience

Unlock Insights with Data Transformation

Data transformation with Trocco turns raw data into analysis-ready formats, enabling businesses to gain insights, understand trends, and customer behaviors for a competitive edge.
It enhances data quality and consistency, significantly improving analysis accuracy, which supports strategic decision-making, risk management, and strategy development.

Enhance Data Quality for Accurate Analysis

Trocco’s transformation feature helps improve data quality as it addresses the inconsistencies, inaccuracies, and duplications found in raw data that can adversely affect analysis and decision-making.
Through data standardization and organization, it enhances the reliability and accuracy of analysis results, paving the way for more effective strategy development and decision-making processes.

Efficient Data Management for Business

Efficient data management is facilitated by Trocco’s data transformation in today's digital-centric business landscape, where data comes from multiple sources and requires uniform metadata.
It simplifies refining metadata and organizing datasets, making data easier to capture, organize, and manage. This ensures seamless business operations and the successful implementation of data-driven strategies.

Add Additional Security Layers

Masking
Hashing (SHA256)
Data type conversion
Programming ETL (Ruby / Python)
String conversion (NFKC)
Record filter
String substitution (regular expression)
Trocco enhances your business data security with additional protection layers.
It safeguards against unauthorized sharing and implements team-specific usage restrictions, improving overall data privacy.
Technical Capabilities

Elevate Your Data Workflow and Streamline Analytics with Automated Schema, Custom Templates, and Dynamic Variables

Anonymize Sensitive Data with Masking
This feature helps in anonymizing sensitive data by replacing the username component of email addresses with asterisks (*), thereby protecting user identity in datasets.
Refine Your Datasets with Selective Filtering
Filtering allows users to selectively exclude rows from datasets based on specific conditions. This is particularly useful for refining datasets and focusing on relevant data.
Enhance Data Security with SHA256 Hashing
Trocco employs SHA256 hashing to secure data. Hashing is a method of converting data into a fixed-size string of characters, which is practically impossible to reverse, offering an additional layer of data security.
Modify Data with Regular Expression String Replacement
This feature enables the replacement of strings in a particular column based on regular expressions. It's highly versatile for pattern matching and modifying data according to complex rules.
Achieve Text Consistency through String Normalization
Using the NFKC (Normalization Form KC) method, Trocco can convert strings to a normalized form, such as transforming full-width characters to half-width characters, which is crucial for consistency and comparability of text data.
Ensure Data Compatibility with Type Conversion
Trocco simplifies the process of changing the data type of source data. This is essential for ensuring that data types are consistent and compatible across different systems or databases.
Simplify Nested Data with JSON Expansion
This functionality expands JSON values into multiple new columns, making it easier to work with nested JSON data by transforming it into a more accessible tabular format.
Customize Data Transformations with Programming ETL
For complex data transformations that cannot be achieved through standard features, Trocco allows writing custom transformations using either Ruby or Python. This offers immense flexibility and power to handle intricate data processing tasks.
See other functions

Unlock Comprehensive Data Management: See What else Trocco offers to you

Workflow
(job management)

Complex pipelines are also defined in the GUI. There are plenty of connections necessary for data engineers
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Data Mart
Generation (ELT)

Just write the conversion process in the SQL editor, and the logs on DWH are easy to use for business.
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Data Catalog

Eliminate bottlenecks from data understanding to use, and shorten the lead time to problem solving
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Data check

Flexible testing and verification of data accumulated on DWH is possible.
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Git and API linkage function

Infrastructure as Code as a data analysis platform. Enhanced API cooperation with external tools
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Operational support (DevOps)

Development of support functions necessary for the operation of data analysis platforms, such as notifications and re-execution, from the engineer's perspective
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Web behavior log
collection SDK

Collect website log events with a dedicated SDK and transfer them to your favorite DWH
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Security

Equipped with functions such as encryption and IP restriction to handle important data
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Support

A dedicated customer will be in charge and support the response and operation of failures.
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Data Mart Generation (ELT)

Just write the conversion process in the SQL editor, and the logs on DWH are easy to use for business.
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Data Mart Generation (ELT)

This feature enables the transformation of data warehouse (DWH) logs into a business-friendly format by simply using an SQL editor for the conversion process. It streamlines the preparation of data marts, making it easier for businesses to analyze and draw insights from their data efficiently.

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Workflow (job management)

Complex pipelines are also defined in the GUI. There are plenty of connections necessary for data engineers
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Workflow (job management)

Users can define complex data pipelines through an easy to use graphical user interface (GUI), facilitating easier management of data flows. It supports numerous connector options, making it a versatile tool for data engineers who need to manage and integrate diverse data sources and processes.Scheduled DWH Queries support the regular execution of queries with customizable variables and Slack notifications to keep teams updated. Furthermore, Job Dependencies integrate with workflow functionality to create dependencies for systematic data aggregation post data transfers, optimizing data processing workflows. This comprehensive approach ensures efficient and effective data management and analysis.

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Data Catalog

Eliminate bottlenecks from data understanding to use, and shorten the lead time to problem solving
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Data Catalog

This function addresses the challenge of navigating and understanding large datasets by eliminating bottlenecks that slow down the transition from data comprehension to utilization. It effectively reduces the time needed to identify and resolve data-related issues, enhancing overall productivity and decision-making speed.Enhanced table previews offer detailed metadata, summary statistics for data distribution insights, and functionalities like filtering and sorting, streamlining data understanding.The query editor caters to engineers with features like auto-completion, query saving, execution previews, and CSV export options, enabling immediate query writing from anywhere within the data catalog.

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Data check

Flexible testing and verification of data accumulated on DWH is possible.
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Data check

Offers the capability for flexible testing and verification of data stored in the data warehouse. This feature ensures the integrity and accuracy of data before it's used for analysis, reporting, or decision-making, contributing to the reliability of data-driven insights for your business. By integrating these checks with workflow functionality, the entire process of data verification and integration is automated, streamlining the monitoring and maintenance of data quality and consistency across platforms.

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Git and API linkage function

Infrastructure as Code as a data analysis platform. Enhanced API cooperation with external tools
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Git and API linkage function

This extends the principles of Infrastructure as Code (IaC) to the realm of data analysis platforms, promoting more efficient and reliable setup and management of data infrastructure. By linking data transfer settings with Git in YAML format, users can achieve version control for their configurations, allowing for changes to be reviewed through pull requests by the team. Furthermore, API integration facilitates job execution and status monitoring, with the capability to specify custom variable settings at runtime. It enhances collaboration and integration with external tools through robust API support, streamlining workflows and facilitating automation.

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Operational Support (DevOps)

Development of support functions necessary for the operation of data analysis platforms, such as notifications and re-execution, from the engineer's perspective
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Operational Support (DevOps)

Utilizing trocco for all operational tasks streamlines processes significantly, eliminating the need for custom development of functions such as periodic execution and error notifications, which are required when using tools like Embulk. Trocco provides a comprehensive suite of necessary operational functionalities, including team-based management of operational permissions for transfer settings and connection information. Its features support regular, scheduled executions, as well as advanced notification systems through Slack or email, which can be triggered by error counts or execution times, and a mechanism to prevent duplicate job executions in closely scheduled scenarios. This cohesive approach enhances efficiency and reduces the complexity of data operations management.

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Web behavior log collection SDK

Collect website log events with a dedicated SDK and transfer them to your favorite DWH
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Web behavior log collection SDK

With a dedicated software development kit (SDK), this tool enables the collection of web logs and event data, which can then be seamlessly transferred to the data warehouse of choice. It's an effective way to gather web analytics data for further analysis and insights. For Example by simply embedding JavaScript tags on your website, you can effortlessly collect essential data for web analysis, including page views (PV), events, and email opening logs, mirroring the comprehensive data collection capabilities of Google Analytics in terms of raw logs, session, and channel definitions. Moreover, these logs can be seamlessly transferred to your preferred Data Warehouse (DWH), provided it is compatible with trocco services, ensuring that your web analytics data is easily accessible for in-depth analysis and decision-making.

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Security

Equipped with functions such as encryption and IP restriction to handle important data
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Security

Recognizing the importance of securing sensitive data, trocco ensures stringent security for data and sensitive information, employing encrypted communication for data in transit and AWS KMS for the encryption and isolated storage of sensitive details like connection passwords. It enhances data integrity and privacy by launching a dedicated server, in the form of a new container, for each data transfer, ensuring no data mix-up between different companies' accounts. Post-transfer, containers are destroyed, minimizing customer data retention time. Furthermore, Trocco® meets rigorous security standards by offering AWS PrivateLink for secure, private internet-free communication with external services, catering to those with strict security requirements and seeking additional safeguarding for their data transfers.

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Support

A dedicated customer will be in charge and support the response and operation of failures.
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Support

trocco offers comprehensive support to ensure clients can effectively utilize our services, including post-implementation onboarding sessions, assistance with specific inquiries, and error analysis, all included within the advanced plan at no extra charges. This type support structure fosters a seamless user experience, and for more interactive and prompt communication, we provide support channels through Slack and email, available during weekday daytime hours, excluding public holidays and weekends. This approach ensures that users receive timely assistance and guidance, enabling them to maximize the benefits of trocco’s offerings.

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How it works

Automated data replication architecture

From source to destination, our core functionality automates the extract and load replication process for all of our connectors, so you can enjoy total pipeline peace of mind.
Data Integration/
Ingestion
Begin by swiftly connecting to any data source, enabling the collection of diverse datasets within minutes.
Data Transformation
Convert the ingested raw data into structured business data models that are ready for analysis.
Data Orchestration
Automate and optimize the entire data flow, from initial ingestion to final storage.

Still curious ?

Watch our live demo video to see the platform in action. Witness firsthand how our ETL pipelines can transform your data processes, making them more efficient and effective.
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Frequently Asked Questions

01.
How to fix the error that occurs when the transfer volume from BigQuery is too large
Note This is a machine-translated version of the original Japanese article. Please understand that some of the information contained on this page may be inaccurate. summary When specifying BigQuery as the transfer source, an error may occur if ...
02.
How to specify elements and extract values when an array is included in the source column JSON
Note This is a machine-translated version of the original Japanese article. Please understand that some of the information contained on this page may be inaccurate. summary If the JSON of the transfer source column contains an array and you wan...
03.
How to generate a webhook URL in Slack
Note This is a machine-translated version of the original Japanese article. Please understand that some of the information contained on this page may be inaccurate. summary Learn how to issue the webhook URL required for notifications to Slack....
04.
Is it possible to increase the transfer speed?
Note This is a machine-translated version of the original Japanese article. Please understand that some of the information contained on this page may be inaccurate. summary When specifying BigQuery as the transfer source, an error may occur if ...
05.
Do you support transfers from an on-premise environment?
Note This is a machine-translated version of the original Japanese article. Please understand that some of the information contained on this page may be inaccurate. summary When specifying BigQuery as the transfer source, an error may occur if ...
06.
Do you support transfers from an on-premise environment?
Note This is a machine-translated version of the original Japanese article. Please understand that some of the information contained on this page may be inaccurate. summary When specifying BigQuery as the transfer source, an error may occur if ...

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