Technology

[technology-posts]

Oracle Changed Data Capture Explained, Technology, Modes, and Benefits

Before going to Oracle Change Data Capture (CDC), it is necessary to understand the concept of CDC. Change Data Capture (CDC) is software design patterns that are mainly used to monitor changes in data in the source database. This is done so that at a later stage, relevant action can be taken on the changes made. Data capture, data identification, and data delivery are all integral components of CDC.

Now coming to Oracle CDC, this technology assists real-time data integration across system networks of enterprises thereby speeding up data warehousing activities and improving manifold the performance and availability of databases. A critical benefit of Oracle CDC is that when replication is done on Oracle database with technologically advanced tools and non-intrusive methods, there is no performance degradation or lag in speeds. These tasks include offloading analytics queries from databases in production to data warehouses and migrating databases to the cloud without stopping work or downtime. Changes made to data, also called incremental data for additions to the databases, can be extracted from different sources and transferred to a data warehouse with Oracle CDC.

One of the main functions of Oracle Change Data Capture is preserving and capturing the state of the data. It can either be in any database or in a data warehouse environment that can also be used in a data repository system. Hence, the task of developers for setting up Oracle CDC mechanisms is made easy as they can avail multiple ways to do so, starting from application logic to physical storage in one or several groupings of system layers.

Modes of Oracle CDC

Oracle Data Integrator supports two journalizing modes.

·        Synchronous Mode: Triggers are placed at the source database. This makes sure that all changes to the data are triggered and captured immediately. Thus, each SQL statement executes a Data Manipulation Language (DML) activity which is inserting, deleting, or updating data. In Synchronous mode, the changed data is taken as a part of the transaction that changed the data at source. This feature of Oracle CDC is included in the Oracle Standard Edition and the Oracle Enterprise Edition.

·        Asynchronous Mode: Data is sent to the redo logs. The changed data is captured after SQL statement is executed through a DML process. Here, the changed data is not captured as a part of the transactions that were responsible for changes to the table at source and therefore, do not affect the transaction. The three modes of asynchronous Oracle CDC are Auto Log, Hot Log, and Distributed Hot Log. The Asynchronous Change Data Capture is based on and offers a relational interface to the now discontinued Oracle Streams.  

Oracle CDC is a very affordable solution as overheads are reduced by simplifying the extraction of the modified data from the Oracle database. It has come a long way from its early stages to optimize database administration, replication, and migration activities.

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Features of Oracle Change Data Capture Explained

Oracle database management system has been optimizing business efficiencies of organizations around the world since it was commercially launched in 1991. This is especially true for enterprises that operate in a fully data-driven environment.

Oracle is the ideal base for running data warehouses, intricate queries, and transaction processing and is known for its open-source platform and technologically advanced features. These include advanced data analysis functionalities, a robust querying layer, access controls, optimized support structure, and most critically, ACID-compliant enterprise-grade security. The combination of a distinct data warehouse and the traditional database has made this database management platform a very popular ETL stack.

Oracle CDC (Change Data Capture)is the technology where real-time data integration across enterprises is improved through the availability and performance of databases by speeding up data warehousing solutions. Many replication processes are done with Oracle CDC without users facing any lag or performance degradation more so when the replication activities are carried out with cutting-edge tools and efficient and non-intrusive methods.

These tasks include offloading analytics queries from databases in production to analytical platforms and data warehouses and migrating databases to the cloud without any downtime or system stoppages. Incremental data can also be extracted from multiple sources through Oracle CDC before being transferred to a data warehouse.

Change Data Capture is an optimized and highly effective solution for reducing data warehousing costs. It is because Oracle CDC extracts and loads data into data storage repositories and any data warehouse incrementally and in real-time. Since the changes that are made only to the database at the source are recorded, organizations need not carry out time-consuming full refreshes to the database every time a change is made at the source.

Change Data Capture is based on the concept that whenever the data stored in one computer or system is changed, another computer has to take some action based on those changes. The first is called the source system and the second the target database. It is quite possible that the source and the target systems are the same and, in this case, too Oracle CDC is very effective.    

CDC explained simply is software design patterns that are used to track changes in the input database so that action taken on the changes made can be done at a later date. It is also the process where data identification, data capture, and data delivery are implemented through data integration for all changes made to the source database.

Oracle CDC optimizes capture and detection of mentions, deletions, and any updates applied to any tables in an Oracle database. This technology is a very important component of Oracle database replication where CDC classifies and records changes made to data in a relational format that is ideal for being used in EAI, ETL, or other applications.

Summing up, the main functions of Oracle CDC is capturing the state of the data and preserving it.

[technology-posts]

Migrating Database to Snowflake from SQL Server

Before going into the intricacies of loading data to Snowflake from Microsoft SQL Server, it is important to understand the concepts of the two platforms.

Microsoft SQL Server

Microsoft SQL Server is a relational database management system and supports all applications on a single machine, regardless of whether it is based on a local area network or the web. The platform is ideally suited for analytics and database transaction processing and delivers the same efficiencies as other leading databases like DB2 of IBM and Oracle database. Given these features, why would businesses want to load data from SQL Server to Snowflake.

Snowflake

Snowflake is a cloud-based data warehousing solution that offers all the cutting-edge and technologically advanced benefits of a cloud provider. The Snowflake architecture matches multiple cloud vendors and hence developers can use the same tools to work on any or all of them.

The platform has separate scalable and flexible computing and storage facilities. Users can work on any of them as per requirement and pay only for the volume of resources used. Further, being cloud-based, it offers unlimited storage and computing capabilities. Businesses do not have to invest in additional hardware and software in case of a sudden increase in demand for resources.

Additionally, a benefit for enterprises to load data from SQL Server to Snowflake is that unstructured, semi-structured, and structured can be migrated to Snowflake, a feature that is not available on Oracle or SQL Server. Snowflake is a high-performing data warehouse solution as multiple users can simultaneously execute intricate queries without any drop in performance.

How to load data from SQL Server to Snowflake

The following are the steps to load data from SQL Server to Snowflake.

·        Extracting data from SQL Server: The data is first extracted from the SQL Server through queries for extraction. For sorting and filtering the data select statements are used. The Microsoft SQL Server Management Studio tool is used to extract large databases and bulk data in CSV, SQL queries, or text format.

·        Formatting the mined data: The data that is extracted cannot be loaded directly into Snowflake as it has to match the data types that are supported by the Snowflake architecture. The data has to be processed and formatted to meet this condition. However, it is not necessary to specify a schema beforehand for JSON and XML data.

·        Staging of data files: The data files have to be kept in a staging area now. For an internal stage, it has to be created with SQL statements while Snowflake supports Amazon S3 and Microsoft Azure as external stages.

·        Data to Snowflake: The final stage to load data from SQL Server to Snowflake is transferring the data from a staging area to Snowflake.

This loading of data from Microsoft SQL Server to Snowflake is fully automated.

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Migrating Database to Snowflake from SQL Server

Managing large databases is the primary task of database administrators. They have to ensure that data is processed quickly and an efficient method to do so is migrating databases from traditional systems to the latest cloud-based ones.

One such likely scenario is migrating database Microsoft Server SQL to Snowflake. More and more organizations are opting for it because of the many benefits provided by Snowflake, a cloud-based data warehousing solution.

Here is what Snowflake has to offer to businesses.

·        The Snowflake architecture is compatible with a range of cloud vendors. Users can use the same tools to work on any of them.

·        The computing and storage facilities are separate in Snowflake and users can scale up or down seamlessly in any of them by paying only for the resources used.

·        An important reason for migrating databases from Server SQL to Snowflake is the high-performing abilities of the data warehouse even when multiple users are simultaneously executing intricate queries.

·        Unstructured, semi-structured, and structured data can be loaded to Snowflake with in-built support provided to JSON, Avro, XML, and Parquet data.

·        Snowflake automatically clusters data without needing indexes to be defined. Various tasks like computing to the encoding of columns are automated too.

How to migrate database Server SQL to Snowflake

This process is mostly automated and can be completed in a few clicks.

·        Extracting data from the SQL Server through queries for extraction with select statements being used to sort, filter, and limit the data during the retrieval process. For exporting large databases in text, CSV, or SQL queries formats, the Microsoft SQL Server Management tool is used.

·        The raw data extracted from SQL Server has to be processed and formatted to match the data types supported by the Snowflake architecture. For JSON or XML data there is no need to specify a schema before loading.

·        The processed MySQL data is then kept in a staging area. There are two staging areas - Internal staging area that has to be created by the user with SQL statements and External staging where data is uploaded using the respective cloud interface. Presently, the external staging areas supported by Snowflake are Microsoft Azure and Amazon S3.

·        The final stage in database migration from SQL to Snowflake is loading the data from the staging area to Snowflake. The data loading wizard of Snowflake is applied for migrating small databases. For large databases and bulk data, however, the Data Loading Overview, a unit of Snowflake has to be used. In this case, the PUT command is used to stage files and the COPY INTO command to load processed data into an intended table.  

After the data is migrated from SQL to Snowflake, a provision should be compulsorily made to load fresh and incremental data as it is not possible to load the whole database every time a change is made to the source data.

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An Overview of Data Replication Benefits

For any business, the very thought of systems crashing and losing data can be a nightmare. But this need not be the case if data is replicated to another location. In the case of an outage in the primary database, the secondary database is triggered and work goes on normally without any downtime till the issue is resolved.

Data replication solutions facilitate the process of copying and storing data in multiple locations. Depending on the needs of an organization, data replication can be a one-time activity or an on-going one. The purpose of replication is to make sure that changes, additions, and deletions to the target database are updated regularly and are consistent with the source database at all times. This in turn helps to improve data accessibility and availability while ensuring system strength and consistency.

Data replication solutions copy data from one location to another. It can be between two on-premise databases in the same location or another, from on-premise to the cloud, from one cloud provider to another, and in very rare cases, from the cloud to on-premise location. Replicating data can be on-demand in batches or bulk according to a pre-set schedule or in real-time where data is entered or modified in the target database at the same time as changes taking place in the source database.

Types of replication

There are several forms of data replication solutions.

·        Full replication – In this process, the entire database is copied from the source database to the target database including new, changed, and existing data. Systems should have high processing power to cope with large data volumes and maintain consistency.

·        Partial replication – This process is faster than full replication as only a part of the data – usually the updated portion – is replicated. Since the volume of data is small there is no additional load on the network.

·        Log-based replication –It is only applicable to databases as it is done through binary files in databases. Data replication solutions read data directly from the blog files, thereby reducing the load on the production system.

Maintaining data consistently across divergent locations can be achieved with highly optimized data replication solutions.

Benefits of data replication

The following benefits can be had from data replication.

·        Data availability and integrity – Data replication assures easy access to data in different locations. It is particularly important for organizations that are spread all over the globe. In case of any system failure, another at a different location is automatically triggered.

·        Disaster recovery – In case of any failure or system outage at a location, data from another location can be used to update the affected system. Data recovery is one of the critical benefits here.

·        Support for shared projects – Modern data-driven businesses replicate data from various sources into data marts and data warehouses to help analytics teams in multiple locations take up shared projects.

These are some benefits of data replication solutions.