HarlowData ETL
for Data Migration Data Integration Data Cleansing
We offer Data Migration, Data Quality Assessment and our Data Tool is perfect for any organisation going through change of systems.
What we do
Empowering Data Projects
We offer Data Migration, Data Quality Assessment and our Data Tool is perfect for any organisation going through change of systems.
ETL
A complete end to end process of extracting data, data mapping, transformation and loading data to target systems.
Data Profiling
Profile your data and review the quality of data to assess completeness and usability. Tools that we use identify duplicates and data value distribution and data length across the data set.
Data Cleansing
Our platform provides all tools required for data cleansing, ensuring high-quality data is produced. Lookup features, de-duplication & custom filtering.
Our Platform
ETL
Empower your creativity and craft stunning sites effortlessly while enjoying real-time customer support at every step.
ETL Process
HarlowDATA is a data management platform that houses data from multiple sources into a single repository.
A software application designed for organisations undergoing data projects that require complete governance and security with a range of features to successfully transform data, map and migrate data.
The Importance of ETL
Organisations today gather both structured and unstructured data from various sources, including:
- Customer data from online payment systems and customer relationship management (CRM) systems
- Inventory and operations data from vendor systems
- Sensor data from Internet of Things (IoT) devices
- Marketing data from social media and customer feedback
- Employee data from internal human resources systems.
The Process Applied:
- Import Data
- Complete Governance
- Set Business Rules
- Data Profiling & Quality
- Cleansing Tools for all types of Data
- Full Reporting and Data Status
By applying the process of extract, transform, and load (ETL), these diverse raw datasets can be converted into a format and structure more suitable for analytics. This transformation enables organizations to derive more meaningful insights. For instance, online retailers can analyze data from points of sale to forecast demand and manage inventory. Similarly, marketing teams can integrate CRM data with customer feedback from social media to study consumer behavior.
Expertly Developed
A complete ETL Solution,
all in a single Application
HarlowData has every feature to complete a Data Migration project. It has been specifically developed with Data Experts with over 30 years of experience.
Data Discovery
Data discovery in ETL involves identifying and understanding the data sources, types, and structures within an organisation. This process includes cataloging data assets, assessing data quality, and uncovering relationships and patterns within the data.
Data Profiling
Data profiling in ETL involves analyzing the data from various sources to understand its structure, content, and quality. This process includes assessing the data for accuracy, completeness, consistency, and validity.
Data Extraction
Data extraction in ETL is the process of retrieving raw data from various source systems, such as databases, files, or APIs. This step involves identifying the relevant data and extracting it in a way that maintains its integrity.
Data Cleansing
Data cleansing in ETL involves detecting and correcting errors, inconsistencies, and inaccuracies in the extracted data. This process ensures that the data is accurate, complete, and reliable by removing duplicates, filling in missing values, and standardizing formats.
Data Validation
Data validation in ETL is the process of ensuring that the data meets predefined quality and integrity standards before it is transformed and loaded into the target system. This involves checking for accuracy, consistency, completeness, and adherence to business rules or constraints.
Data Mapping
Data mapping in ETL is the process of defining how data from source systems is transformed and loaded into target systems. This involves specifying the relationships between source data fields and target data fields, including any necessary transformations or business rules.
Data Transformation
Data transformation in ETL is the process of converting extracted data into a format suitable for analysis and reporting. This involves applying various operations such as filtering, aggregating, sorting, and enriching the data to align it with the target system's structure and requirements.
Data Reconciliation
Data reconciliation in ETL is the process of ensuring that the data loaded into the target system accurately reflects the source data. This involves comparing and validating the data at various stages of the ETL process to identify and resolve discrepancies.
Data Import
Data import in ETL is the process of loading transformed data into the target system, such as a data warehouse or database. This step ensures that the cleaned and structured data is integrated into the target environment, making it accessible for analysis, reporting, and decision-making.
Resources
Data Cleansing Dashboard
Transform the way Data is Cleansed, without excessive use of Spreadhseets and Tracking of Changes.
Harness the power of Data Cleansing intelligence to gain insights from any Dataset
Data cleansing in ETL involves detecting and correcting errors, inconsistencies, and inaccuracies in the extracted. Effective data cleansing improves the quality of the data, making it suitable for transformation and analysis, which leads to more precise and actionable insights.
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