The only all-in-one data pipeline platform

Data Migration

Out of the box data flows covering logical and physical mappings between applications such as Oracle Cloud Applications, SAP S/4HANA, Oracle E-Business Suite, SAP ECC, Oracle JD Edwards, Microsoft Dynamics, Oracle Peoplesoft, Oracle Siebel and many other enterprise application environments. Reference data migration mappings are also available

Data Extraction

PiLog provides pre-defined adapters for more than 50+ applications for extracting master data, meta data and transactional data. Setup data are extracted from major ERPs. These adapters support multiple connection methods such as web services (REST, SOAP), JDBC, JSON, XML, Excel, flat files, BAPI, OData, MQ, JMS Queues etc.


Pre-Validation transforms the data into a format that is more easily and effectively processed. Pre-validation also checks for technical data issues such as: constraints, data types, length, null check, format mask for date and time, check text case, duplicate data, currency rounding etc. ensuring the quality and integrity of data, the benefit of this is to understand issues before loading the data into the target application. Additionally, it enables users to configure additional Pre-Validation rules based on business needs.


Transformation converts data, typically from the format of a source system into the required format of a destination system, rules engine enables users to configure lookup and expression-based transformation rules. Internal or external data can be used for transformation logic. Transformation also depends on various factors like how often source data change does, how differently is data stored in the target system and also the volume. we also provide pre-defined transformation logic for a majority of the source to target migration project needs.

Data Loading

PiLog provides data loading adaptors for many enterprise application environments that pushes processed, cleanse and accurate data. These adaptors support setups, master and transactional data (both open and historical) loading.

We also provide high performance packages to load High data volumes within a short time.

Data Health Assessment & Data Quality

Data Quality Management is aimed to automate the process of standardization, cleansing & management of unstructured/free text data by utilizing ASA (Auto Structured Algorithms) built on PiLog's taxonomy and the catalog repositories of master data records.

Data Profiling

Data profiling is the process of examining and analyzing data that gives insights which involves machine learning procedures for Data Understanding, Visualization, Missing Data findings, Outliers Detection, Quality and consistency, Dimensionality Reduction, Reporting and Sampling of data.

Data Cleansing

Initially this starts with fixing bad data in the data set by standardizing the attributes based on the requirements by de-duplicating and processing the data by avoiding unwanted characters, double spaces, trailing whitespaces and checking data type of each column which encapsulates any field that can't be neatly fit as numerical or categorical data. This also Includes element-wise cleaning to get them to a uniform format to get a better understanding of the dataset and enforce consistency., renaming columns to a more recognizable set of labels, index changing that helps in using uniquely valued identifying field of the data as its index, cleansing columns data and whole data set cleansing by examining the distribution of missing values across all the rows of dataset

Data Fetching

Sometimes Data from legacy systems seems incomplete with some of the attributes that enhances the visualization, in such cases data is enriched from renowned sources to generate meaningfull master data. Adding country codes for telephone numbers in good formats can be considered as one such case

Data Validation

An ML driven approach where it assesses data quality, correctness and consistency based on the ML techniques written considering the formats for each attribute.Rule-based validation algorithm, Entity recognition algorithm, Type and Quality assessment algorithm, Pattern and Label based Algorithms helps in performing successful data validations.

Data Migration Modified Text

Data migration is a tactical process of transferring data from one system to another new or upgraded system. PiLog Data migration service effectively selects, adapts and transforms data from one system storage to another permanently and has the ability to handle complex Data Migrations. Inflated demand of enterprises on optimization and technological advancement, employing database migration services to move their on-premises infrastructure to cloud-based storage. Storage and cloud migrations sort of migrations ensure businesses with opportunities to increase their coordination, intensify growth, and discover business advantages. Data migration offers cost-effective transmission of applications to an upgraded and innovative context.