What is the recommended method to ingest binary files from mainframes can sqoop handle this type of
These files may be delimited text files for example, with commas or tabs separating each field , or binary Avro or SequenceFiles containing serialized record data. Sqoop imports data in parallel from most database sources. You can specify the number of map tasks parallel processes to use to perform the import. By default, Sqoop will identify the primary key column if present in a table and use it as the splitting column.
Sqoop provides an incremental import mode which can be used to retrieve only rows newer than some previously-imported set of rows. You can import data in one of two file formats: Delimited text is the default import format. SequenceFiles are a binary format that store individual records in custom record-specific data types. Large objects can be stored in-line with the rest of the data, in which case they are fully materialized in memory on every access, or they can be stored in a secondary storage file linked to the primary data storage.
By default, large objects less than 16 MB in size are stored in-line with the rest of the data. Sqoop can also import records into a table in HBase. Sqoop imports data in parallel by making multiple ftp connections to the mainframe to transfer multiple files simultaneously. The target table must already exist in the database. The input files are read and parsed into a set of records according to the user-specified delimiters.
At a smaller scale, a staging database is a separate storage area created for the purpose of providing continuous access to application data. Using a staging database, you can for example prevent interruption of services to your websites while new business data is updated and tested. Changes may then be approved, before incorporating them into the real-time environment. Overall, staging areas are intended to increase the efficiency of ETL processes, ensure data integrity and support data quality operations.
Access to data can continue, even when new data is being imported from various external sources in preparation for staging. Using a staging database is beneficial when you have several satellite sites located in different cities and time zones. All sites may be updated from one location at the same time, without losing crucial data which might result in costly downtime.
Another objective of the staging area is to minimize the impact of data extraction on your source system. After you extract data from your source system, a copy of it is stored in your staging database. Should your ETL fail further down the line, you won't need to impact your source system by extracting the data a second time.
And if you store the results of each logical step of your data transformation in staging tables, you can restart your ETL from the last successful staging step. Staging areas can assume the form of tables in relational databases, text-based flat files or XML files stored in file systems, or proprietary formatted binary files stored in file systems. Functions of the staging area include:.
The staging area acts like a large repository, and it's common to tag data in the staging area with additional metadata.
This indicates the source of origin, and has timestamps indicating when the data was placed in the staging area. This is a function closely related to, and acting in support of, master data management capabilities.
The alignment of data involves standardization of reference data across multiple source systems, and the validation of relationships between records and data elements from different sources. This is done by taking advantage of data streaming technologies, reduced overhead from minimizing the need to break and re-establish connections to source systems, and the optimization of concurrency lock management on multi-user source systems.
In some cases, data may be pulled into the staging area at different times to be held and processed all at once. This might occur when enterprise processing is to be done across multiple time zones each night, for example. In other instances data might be brought into the staging area to be processed at different times.
In addition, the staging area may be used to push data to multiple target systems. The staging area supports reliable forms of change detection, like system-enforced timestamping, change tracking or change data capture CDC. Often, the target data systems do not. The ETL process using the staging area can be employed to implement business logic which will identify and handle "invalid" data spelling errors during data entry, missing information, etc. Data cleansing, also called data cleaning or scrubbing, involves the identification and removal or update of this data.
Often, much of the cleaning and transformation work has to be done manually, or by low-level programs that are difficult to write and maintain. Third-party tools are available, and should be used to limit manual inspection and programming effort. Ideally, an analytics platform should provide real-time monitoring, reporting, analysis, dashboards, and a robust set of predictive tools that can support the making of smart, proactive business decisions. These solutions should include a flexible data warehousing infrastructure, so that users can access the information they need without worrying about delays, or system disruptions during an upgrade, improvement or disaster.
Transactional data, data warehouses and analytic tools are located on one platform, which scales easily to create a single copy of data that can be accessed by multiple users and groups. A single analytics tool can then be used across an entire organization, ensuring consistent business rules. IT organizations often take data that's been captured by the mainframe, and move it to distributed servers for processing. Furthermore, spreading analytic components across multiple platforms when preparing it for analysis degrades the data quality.
This degradation becomes worse, when multiple copies of the data are being created to support development, test and production environments. These make it possible to minimize data latency, complexity and costs by bringing data transformation and analytic processes to where the data originates. Veristorm, an IBM partner, enforces what it calls an "anti-ETL" strategy - in direct contrast to legacy ETL products which transform data on the mainframe then stage it in intermediate storage before it's ready to be loaded to a target site.
The Apache Hive data warehouse software is an open source volunteer project under the Apache Software Foundation.