sap smart data integration

•Finally, the Data Loading Overview tool in Snowflake guides users through the process to load data from SQL server to Snowflake. A PUT command is used to stage files and COPY INTO command to load processed data into an intended table. At first glance, the whole process might seem overwhelming but in reality, it can be completed in a few clicks only.

Microsoft SQL Server database supports applications on a single machine either on a local area network or across the web. It combines data seamlessly into the Microsoft ecosystem, supporting Microsoft’s .NET framework. Steps to migrate data SQL Server to Snowflake – • Extract data from SQL Server – Generally, those working with databases use queries for extracting the data from the SQL Server. Select statements are utilized to sort, filter, and limit the data that needs to be retrieved. However, the Microsoft SQL Server Management Studio tool is deployed for exporting bulk data or entire databases in formats such as SQL queries, text, or CSV. • Process data for Snowflake – Before you migrate data SQL Server to Snowflake, the data has to be processed and prepared for loading. This step depends largely on the data structures which have to be verified to know whether this data type is supported by Snowflake for seamless migration and loading. However, for loading JSON or XML type data into Snowflake, no schema has to be specified beforehand. • The Staging Process – Before data can be loaded from SQL Server to Snowflake, the data files have to be first stored in a temporary location. There are two possibilities here. >Internal Stage – For ensuring greater flexibility while migrating data from SQL Server, an exclusive internal stage is created with respective SQL statements. Quick data loading is thereby ensured by assigning file format to named stages. >External Stage – Presently, only two external staging locations are supported by Snowflake, namely Microsoft Azure and Amazon S3. • Migrating data into Snowflake – A part of Snowflake’s documentation is Data Loading Overview. It guides the user through the whole process to migrate data SQL Server to Snowflake. For small databases, utilizing the data loading wizard is advisable. When the data to be loaded is very large, the following options should be used. > Use the PUT command to stage files. > Copy from Amazon S3 or the local drive when the data is lodged in an external stage > Use the COPY INTO table command when processed data has to be loaded into an intended table. An advantage of this process is that you can make a virtual warehouse that powers this migration activity.

Snowflake runs on Amazon Web Services EC2 and S3. It is a cloud-based data warehouse and has separate compute and storage resources. Users have the flexibility to scale up and down depending on the quantum of data and pay only for the resources used. It can load both structured and unstructured data with multiple workloads operated by multiple users working together without any drop in performance in Snowflake. A major advantage of Snowflake is that it can automatically create tables and columns and detect schema changes. The snowflake table is always kept updated with the most accurate data types and data loading and processing are done quickly and in real-time.  A few specific steps have to be followed for loading SQL to Snowflake. The first is to get the data out of the SQL Server. The traditional way is queries for extraction through filtering, sorting, and limiting the data that has to be retrieved. Microsoft SQL Server Management Studio tool is used for bulk export of data, databases, and entire tables. Formats relied upon for this activity are text, CSV, or SQL queries that can restore the databases when loaded to Snowflake. Once the data is extracted, the next step before transferring SQL to Snowflake is the preparation of the data. The amount of work that needs to be done now depends on the existing data structures. Hence it is essential to confirm the data type for Snowflake to ensure that the new data matches accurately with it. A Schema should be fixed in advance before loading data into Snowflake. Once these two steps have been completed the process of SQL to Snowflake transfer of data can be taken up. The Data Loading Overview of Snowflake will guide the user through the data loading process. The COPY INTO TABLE command loads the ready data into a pre-prepared table while the PUT command is used to stage the files. The data can be copied from Amazon S3 or the local drive.

Change Data Capture (CDC) is a technology that detects and records changes that have been made to a database and replicates them to other applications and databases. Integrating SQL server replication and CDC replication, SQL server CDC replication is a technology in-built in the SQL Server that records, inserts, updates, and deletes activities applied to a user table. It then stores this data in a form that can be used by an ETL application such as SQL Server Integration Services (SSIS). Change Data Capture when applied to SQL Server tables gives an account of what changed, when, and where in simple relational “change tables”. These change tables have columns that imitate the column structure of the source table chosen along with the metadata to understand the changes that are made. The native change data capture feature offered by Microsoft is not the only CDC in SQL Server environments available today. Currently, there are several solutions offered by third-parties for both SQL database replication and CDC that provide the same Microsoft’s in-built SQL server CDC replication functionality. Businesses often use applications where it is required that the value of data in the database be recorded before it is changed that is, the history of all changes must be saved. The 2005 version of SQL Server almost found a solution by introducing “after update”, “after insert”, and “after delete” triggers. An improvement was made in SQL Server 2008 through Change Data Capture (CDC) that enabled SQL Server developers to deliver SQL Server data archiving and capturing without any additional programming. SQL server CDC replication tracks only the changes made in user-created tables. Here are some tips to optimize CDC in SQL Server replication. Make sure that you have already enabled SQL Server Agent before allowing Change Data Capture at the table level. Next, change settings so that the history data from the auxiliary change table is removed. If not the history data might grow in size to become unmanageable, thereby decreasing the overall performance of the replication script. All these issues can be avoided though. Compulsorily use the primary keys in the tables only.

Replication is the technology that processes, copies, and distributes database objects from one database to another. It is also used to distribute data to different locations and remote and mobile users. It is done through the Internet, dial-up connections, wireless connections, and local and wide area networks. Before the era of SQL server data replication, organizations had a single server for multiple users at various locations resulting in performance and availability problems and maintenance issues. Now SQL server replication tools help to maintain database copies at multiple locations. SQL Server data replication improves the performance of SQL Server databases and the availability of information across an organization. It ensures that businesses can integrate and load data into data warehouses, migrate data to cloud storage platforms, and copy and distribute data across multiple database servers in remote locations. These activities are regardless of whether a third-party strategy is created by a replication expert or the process is implemented using built-in MSSQL replication utilities. Enterprises can also accelerate, augment, and reduce the cost of SQL data replication using SQL change data capture technology (CDC). SQL Server data replication CDC also makes sure that data-driven enterprises integrate data in real-time and realize faster time-to-insight. It is because changes and updates that are made by individuals at various locations are synchronized to the main server quickly as SQL Server replication distributes the relevant part of the tables and views only and not the entire database. There are three types of SQL Server data replication. The first is Snapshot Replication. A “snapshot” of the data in one server is taken which is then moved to another server or another database in the same server. Another is Transactional Replication where after the initial snapshot replication, only the changes, updates, or deleted records on the publisher are forwarded to the subscriber/s. Finally, there is Merge Replication where data from multiple sources are merged into a single database. SQL Server data replication synchronizes data across enterprises and replication to SQLCE 3.5 and SQLCE 4.0 are supported by both Windows Server 2012 and Windows 8.

A new feature – Database Replication – has been launched recently by Snowflake. Those using the Standard version and above of Snowflake get the added benefits of non-business continuity and disaster recovery scenarios. It ensures data portability to facilitate migrations and includes secured data sharing across clouds and regions. The existing Enterprise for Sensitive Data (ESD) version will now be termed Snowflake Business Critical (BC) edition. It has a new feature called Database Failover and Fallback which offers business continuity. Organizations are charged for this feature only if used. There are several benefits of data replication to Snowflake. • Immediate Recovery in case of an outage – In case of an outage, the Database Failover, and Failback feature ensures instant failback and failover operations for seamless data recovery. Users can initiate a database failover to get access to secondary databases that are available in the region. These secondary ones become primary databases for write overloads. When the outage is resolved, users perform database failback which is a failover in the reverse direction to enable resumption of normal business operations. • Refreshing data and near-zero data loss – Users can decide the frequency and periodicity at which data replication to Snowflake will be run. It helps to meet specific requirements for data freshness (data sharing use case) or maximum acceptable data loss (Business Continuity Disaster Recovery use case). Snowflake replication supports incremental refreshes only. Here, the changes made after the last refresh are replicated only, thereby quickening the replication process.  • Real-time Replication – An advantage of data replication to Snowflake is that the process takes place in real-time and in instances of data recovery, the time taken is not dependent on the volume of data. When one region faces a disaster or outage, organizations can immediately access and control data that has been replicated in a different cloud service or region.  Snowflake is structured to be a complete SQL database. It works well with Excel, Tableau and other tools that any user will be familiar with. All requirements of the SQL database are met by Snowflake through query tools, full DML, multi-statement transactions, and role-based security support.

There are many integration tools available to organizations but all of them essentially offer the same solutions. The primary technologies for data integration are Extract, Transform, Load (ETL), Enterprise Application Integration (EAI), and Enterprise Information Integration (EII) which is commonly known as data visualization. Here are some of the most used data integration products used by businesses though not in any particular order. Linx – It is a low code platform that can easily build and manage the integration process for systems like files, databases, APIs, third-party libraries, or custom apps. Linx can also connect visually back-end data in databases, XML, HTML, Web services, JSON, and even specific back-end as a service. The product creates, publishes, and manages APIs throughout their life-cycle and has a separate run-time environment to deploy automated integrations and custom applications. CloverDX – This is the perfect data integration platform for businesses needing high-quality cutting-edge technology to solve complex problems in intensive environments. CloverDX can automate and orchestrate transformations and processes, code when needed, and work together with developers and less expensive teams. It blends seamlessly into the existing IT infrastructure and helps to build extensive frameworks to save money. But the critical advantage here is that it hosts in the cloud or on-premise and scales across cores or cluster nodes. Improvado – Among all data integration products, Improvado can pull data from other marketing platforms like Facebook, Google Analytics, Ad Servers, CRMs, email platforms, and more and channel it into any data warehouse or visualization tool of choice. Organizations use Improvado widely as it saves hours of manual reporting and millions of dollars in marketing spend, integrating more than 180 marketing platforms and aggregating all marketing data at one place in real-time. Most importantly, Improvado does not need any developers – it is simply plug-and-play. Integromat – This is a powerful data integration tool with a user-friendly interface, simple to use and yet robust enough to handle complex workflows. Its main features include HTTP, SOAP, JSON modules that enable connectivity with any data source, robust error handling, creation of complex data integration workflows, and Excel-style functions to manipulate data. These are some of the most trusted and reliable data integration products being used by businesses today.  

Transactional Replication – This tool copies data from the publisher to the subscriber/s once and then delivers transactions to the subscriber/s as and when they occur on the publisher. The first copy of the data is transmitted through the same process as snapshot replication. Subsequently, when database users insert, update, or delete records on the publisher, transactions are forwarded to the subscriber/s. Further, the main advantage here among other SQL server replication tools is that by making a simple configuration change, transactions can be delivered continuously. Typically, database servers on transactional publications do not modify data and use it for read-only purposes. Merge Replication – This technology merges data from multiple sources into a single central database. As in transactional replication, this tool also synchronizes data initially by taking a snapshot on the publisher and moving it to subscribers. But unlike transactional replication, merge replication allows changes of the same data on publishers and subscribers, even when the subscribers are not connected to the network. When they connect to the network, replication will notice and combine changes from all subscribers and change data on the publisher accordingly. This tool is useful when data on remote computers have to be modified and when subscribers are not assured of continuous connection to the network. These are some of the most-used SQL server replication tools.

Now, what is SAP data replication? In a nutshell, it is the continual and recurring copying of data from a database in one computer or server to another database in a separate location so that all users regardless of where they are can have access to the same information. The advantage here is that the replication process leads to a distributed database enabling all users to individually access the data without interfering in the work of others. However, replication has its complexities and can be done only by using advanced data replication software. Without it, businesses can run into problems. Some choose RDBMS and data warehouse while others operate on other platforms that are best in tune with their business requirements. This leads to multiple issues with data management. By using replication software it is possible to integrate, distribute, centralize and synchronize data across various data stores and systems. The main benefit of SAP data replication is its ability to synchronize data across multiple databases, data warehouses, and other platforms. It also enables real-time data integration and improves information availability and accessibility across organizations. The software also reduces the costs of implementing data warehousing activities. Additionally, it also supports data security, data resilience, and business continuity by quickly creating a data replica at a location that is in no way linked to the source of the data. This is unlike the conventional backups where copies of data are retained for extended periods. With the software, data replication is done in real-time, more frequently and consistently. Top features of SAP data replication include the ability to continuously replicate data with many recovery points as well as cross-platform replication like a disk to the cloud or vice versa. Another important feature is the zero-downtime data migration and multi-site replication for business continuity in case of a site disaster.     SAP data replication is necessary for long-term data retention and archiving. It also capably provides real-time transactional data delivery and integration into data lakes to support big data initiatives.

Log-based CDC – Some replication tools depend on even-based processing and triggers and are invasive and impose significant overheads. However, log-based Changed Data Capture (CDC) is widely recognized as a superior approach because it minimizes the load on sources and targets and is quite easy to set up and maintain. Initial data load – Top class data replication tools should also feature online data load to initially load target databases. These will compare and provide repair capabilities to ensure that sources and targets always remain in sync. Cost – Apart from the initial license cost, check on the ongoing expenses associated with training and administration. As you research more, you will find white papers and documents on various types of data replication tools.  

Take any Data Warehouse (DWH) – it has to keep a track of all business measure changes. Hence, the ETL processes of DWH loading should be able to notice all data changes which have occurred in source operating systems during the business operations. This is ensured by change data capture which facilitates the insertion of new records, updating one or multiple fields of existing records, and deletion of records.  When CDC records insertion, updating and deletion of activities applicable to a SQL Server table, all details of the changes made are available in an easily consumed relational format. Column information and the metadata that is required to apply the changes to a target environment is captured for the modified rows and stored in change tables that mirror the column structure of the tracked source tables. Hence, table-valued functions are provided to consumers that allow systematic access to changed data.          

# Transactional Replication – Here, data is copied from the publisher to the subscriber(s) once and subsequently transactions are delivered to the subscriber(s) as when transactions take place on the publisher. The initial copy of the data is transported as in snapshot replication in the SQL server.  # Merge Replication – This SQL server data replication tool combines data from multiple sources into a centralized database. Unlike transactional replication, this tool allows modification of the same data on publishers and subscribers, even though subscribers may be offline and not connected to the network. These are some of the important tools of SQL server data replication

In the past, an ETL tool to transform and replicate data had to be installed for loading a flat-file into HANA from the source to the target system. Then, another SLT/SRS was required for real-time functionalities. Hence, several tools and connectors were needed to complete one task, thereby increasing the processing time. This rather cumbersome process has now been sharpened with the SAP data integration tool. The tool acts as a link between source and HANA, reading source type data and transforming it into HANA type data value. Therefore, the requirement of several tools has now been replaced by one. Further, SAP data integration (SDI) replicates and/or batch loads data changes from the source to the SAP HANA tables in real-time. It helps to offer an instant real-time view of the changed and modified conditions both at source or target SAP HANA. Here are some features of SDI that have made it the preferred tool of modern businesses. # Change Data Capture – In a setting where the entire source data has to be replicated to the target every day, a lot of time is devoted to this task, taking the focus away from other core activities. On the other hand, Change Data Capture (CDC) continually tracks modified data and transfers them across the source and target. This ensures that the updated data is available at all times facilitating quick and considered decision-making. # Setup and configuration – Setting up and configuring SAP data integration tool is a very simple process. The primary SAP SDI Agent has to be installed first and the SFDC Adapter deployed over it. The Adapter is the bridge between HANA and the source, is hosted on the S/HANA data provisioning agent and helps to read source data and change the values into HANA data type.  # Dynamically adjusted communication method - The “Maximum Expected Number of Record” parameter is automatically switched between source APIs by the adapter. In the event of the volume of records in source data being equal to or less than the value of the maximum expected records parameter, SOAP API is generally used. If not BULK API is triggered and applied. These cutting edge features make SAP data integration a very powerful tool and businesses can increase performance and productivity through optimized data management.

In today’s digital business environment, most organizations regardless of size or scale are opting to host key SAP systems in the Amazon Web Services Cloud as it helps to speedily provision an SAP environment. Further, the flexibility offered by AWS Cloud enables businesses to scale computing resources up and down as needed. As a result, organizations can allot more resources to core business functions and innovations, thereby leading to optimized growth and development. Most SAP systems operate regular and daily business transactions which are critical for the functioning of an organization. SAP Customers, therefore, get an opportunity to track and troubleshoot these transactions almost on a real-time basis. The AWS data provider for SAP tool collects key performance data that SAP applications can use to monitor all transactions built by SAP. Data from the AWS Data Provider for SAP is read by the SAP Operating System Collector (SAPOSCOL) and the SAP CIM Provider. Before creating an SAP instance, a few technical requirements have to be met. You have to deploy SAP systems that get information from the AWSC data provider for SAP within a VPC (Virtual Private Cloud). Several network topologies help to route Internet-based endpoints. The first is topology that configures routes and traffic directly to the AWS Cloud via an Internet gateway within a VPC. The second topology takes traffic from the VPC, through the organization’s on-premise data center and back to the Cloud.  For all this to happen, you have to grant the AWS data provider for SAP read-only access to the Amazon CloudWatch, Amazon Simple Storage Service (Amazon S3) and Amazon EC2 services so that their APIs can be used. For this, an AWS Identity and Access Management (IAM) role for your EC2 instance have to be created and a permission policy attached to it.  

Types of SQL Server Replication Tools There are three distinct types of SQL server replication tools. # Snapshot Replication This tool simply takes a “snapshot” of the data on one server and moves it to another or another database in the same server. After the first synchronization snapshot, it is possible to periodically refresh data in published tables as per the pre-programmed schedule. This is the easiest of all replication tools but comes with a rider - all data has to be copied each time a table is refreshed. Between scheduled refreshes in snapshot replication, the data on the publisher might be different from the data on the subscriber.   # Transactional Replication In transactional replication, data is copied from the publisher to the subscriber(s) once and then transactions are delivered to the subscriber(s) as and when they take place on the publisher. The initial copy of the data is transported in a similar process as snapshot replication where the SQL server takes a snapshot of data on the publisher and then forwards it to the subscriber(s). But here, as and when database users update, insert or delete records on the publisher, transactions are forwarded to the subscriber(s). # Merge Replication In this technique, data from multiple sources are combined into a single central database. As in transactional replication, merge replication also uses initial synchronization through the snapshot of data on the publisher and moves it to subscribers. But unlike transactional replication, this method permits modifications of the same data on publishers and subscribers, even though subscribers might not be connected to the network. Whenever subscribers connect to the network, merge replication tool will notice it and combine changes from all subscribers and change the existing data on the publisher. Of all SQL server replication tools, merger replication is very useful if there is a need to modify data on remote computers even in the absence of continuous connection to the network. There are some of the important concepts of SQL server replication tools.          

Automated SAP data integration can process structured data from relational databases and applications, both SAP and non-SAP very quickly. Based on the source of the data, it is capable of using three types of data replication – log-based, trigger-based, and ETL-based. The relocated structured data is housed directly in memory. This is why data stored in applications that use HANA can be used quickly in real-time. SAP HANA supports a wide range of cases for real-time analytics. These include – • Supply chain and retail optimization • Forecasting and profitability reporting • Fraud detection and security • Optimization and monitoring of telecommunication network • Optimization and monitoring of energy use          SAP HANA smart data access enables remote data to be accessed similar to local tables in SAP HANA without copying the data into SAP HANA. This provides a great deal of cost and operational benefits. It supports the deployment and development of the next-gen analytical applications that require the capability to synthesize, access, and integrate data from multiple systems in real-time. This is regardless of what systems are generating it or where the data is located. As distinct from other RDBMSs, SAP HANA reduces the memory usage factor by 10. This leads to improvement in performance as it uses column-oriented storage which combines OLAP and OLTP into a single structure.