Flat files in database

For date, timestamp and time data types, the values are expected to be in a specific format so that they can be parsed by the consuming process. Example: An ID column of the flat file is expected to have only numbers. However, few rows in the flat file have characters. Length of string and number data values in the flat file should match the maximum allowed length for those columns. Example: Data for the comments column has more than characters in the inbound flat file while the limit for the corresponding column in the database is only characters.

Example: Date of Birth is a required data element but some of the records are missing values in the inbound flat file. ETL Validator provides the capability to specify data type checks on the flat file in the flat file component.

Based on the data types specified, ETL Validator automatically check all the records in the incoming flat file to find any invalid records. The purpose of Data Quality tests is to verify the accuracy of the data in the inbound flat files. Check for duplicate rows in the inbound flat file with the same unique key column or a unique combination of columns as per business requirement. Sample query to identify duplicates assuming that the flat file data can be imported into a database table.

Flat file standards may dictate that the values in certain columns should adhere to a values in a domain. Verify that the values in the inbound flat file conforms to reference data standards. Many data fields can contain a range of values that cannot be enumerated. However, there are reasonable constraints or rules that can be applied to detect situations where the data is clearly wrong.

Instances of fields containing values violating the validation rules defined represent a quality gap that can impact inbound flat file processing. Example: Date of birth DOB. This is defined as the DATE datatype and can assume any valid date. However, a DOB in the future, or more than years in the past are probably invalid.

Also, the date of birth of the child is should not be greater than that of their parents. The goal is to identify orphan records in the child entity with a foreign key to the parent entity. Example: Consider a file import process for a CRM application which imports contact lists for existing Accounts. ETL Validator supports defining of data quality rules in Flat File Component for automating the data quality testing without writing any database queries. Custom rules can be defined and added to the Data Model template.

Data in the inbound flat files is generally processed and loaded into a database. In some cases the output may also be another flat file. The purpose of Data Completeness tests are to verify that all the expected data is loaded in the target from the inbound flat file. Some of the tests that can be run are : Compare and Validate counts, aggregates min, max, sum, avg and actual data between the flat file and target.

Column or attribute level data profiling is an effective tool to compare source and target data without actually comparing the entire data. It is similar to comparing the checksum of your source and target data. These tests are essential when testing large amounts of data. Some of the common data profile comparisons that can be done between the flat file and target are:. Example 1: Compare column counts with values non null values between source and target for each column based on the mapping.

It is also a key requirement for data migration projects. Example: Write a source query on the flat file that matches the data in the target table after transformation. It takes care of loading the flat file data into a table for running validations. Data in the inbound Flat File is transformed by the consuming process and loaded into the target table or file.

It is important to test the transformed data. There are two approaches for testing transformations — white box testing and black box testing.

For transformation testing, this involves reviewing the transformation logic from the flat file data ingestion design document and corresponding code to come up with test cases.

The advantage with this approach is that the tests can be rerun easily on a larger data set. For these reasons, flat file databases are widely used to store internal configuration data, log data or streaming data in situations where using a relational database would be overkill.

If you have information that does not require multiple tables to represent, using a flat file database allows you to quickly and easily import this data into a target data warehouse or data lake. On the other hand, flat files are not a good option if you want to avoid data redundancy and duplication.

This is because they can only contain a single relational table. Flat files also cannot enforce various database relationships and constraints; this will require the use of an actual relational database management system RDBMS.

Flat file databases are simpler in design and usually smaller than their relational database counterparts. These qualities make flat file databases an appealing choice for many basic uses, including as part of an ETL and data integration strategy. When you're ready to integrate flat file databases into your data integration workflow, Integrate.

The Integrate. Want to know how else Integrate. An Excel spreadsheet, in which each row is a record and each column is a field, can be considered a flat file.

There are certain advantages to such methods of data storage. For one thing, all the records can be stored in one place. They are also easy to set up, without needing specific expertise, and can be easily understood. Since they are independent, self-contained files, they require no outside storage configurations, and can be readily edited and accessed.

However, flat file databases have some major disadvantages and are generally not effective for large-scale record-keeping. Some problems that can arise with their use include the possibility of duplication and the difficulty of keeping records unique.

This can lead to wasted storage and levels of inefficiency. Furthermore, it can be difficult to make changes in the format of the data entered, and in the retrieval of any data requiring multiple queries.