Essential Ingredient for Information Agility. Change Data Capture CDC allows for real time data to be available for any type of data integration solution.
Change Data Capture is accomplished by capturing just the changed records and not the full data set, dramatically reducing time and resources over the life of the data integration solution. Steps to Easy CDC 1. Design or generate Mappings 2.
Select Journalized Data Only 3. Start Journals. Design mappings and check flow integrity 3. Audit, cleanse or recycle rejected records. Large File arrives, detected 4.
ODI transforms payload 2. Pass XML payload, by 8. File arrives, detected by 4. ODI inserts payload to DB 2. An event occurs which 6 prompts a data load Confirmation Shared 2. ODI transforms the data 6. ODI confirms job complete 7. Unique ID for c existing objects Confirmation Shared 4. Synapse Studio Overview hub Overview Hub It is a starting point for the activities with key links to tasks, artifacts and documentation Pin selected ones for quick access Synapse Studio Data hub Data Hub Explore data inside the workspace and in linked storage accounts Data Hub — Storage accounts Preview a sample of your data Data Hub — Storage accounts See basic file properties Data Hub — Databases Explore the different kinds of databases that exist in a workspace.
Starting from a table, auto-generate a single line of PySpark code that makes it easy to load a SQL table into a Spark dataframe Data Hub — Datasets Orchestration datasets describe data that is persisted. Once a dataset is defined, it can be used in pipelines and sources of data or as sinks of data. Synapse Studio Develop hub Develop Hub Overview It provides development experience to query, analyze, model data Benefits Multiple languages to analyze data under one umbrella Switch over notebooks and scripts without loosing content Code intellisense offers reliable code development Create insightful visualizations Develop Hub - Notebooks Configure session allows developers to control how many resources are devoted to running their notebook.
Develop Hub - Notebooks As notebook cells run, the underlying Spark application status is shown. Providing immediate feedback and progress tracking. Dataflow Capabilities Handle upserts, updates, deletes on sql sinks Add new partition methods Add schema drift support Add file handling move files after read, write files to file names described in rows etc New inventory of functions for e. Develop Hub - Data Flows Data flows are a visual way of specifying how to transform data.
Provides a code-free experience. Real-time publish on save Synapse Studio Orchestrate hub Offers a wide range of activities that a pipeline can perform. Synapse Studio Monitor hub Monitor Hub Overview This feature provides ability to monitor orchestration, activities and compute resources. Monitoring Hub - Spark applications Overview Monitor Spark pools, Spark applications for the progress and status of activities Benefits Monitor Spark pools for the status as paused, active, resume, scaling and upgrading Track the usage of resources Synapse Studio Manage hub Manage — Linked services Overview It defines the connection information needed to connect to external resources.
Manage — Access Control Overview It provides access control management to workspace resources and artifacts for admin and users Benefits Share workspace with the team Increases productivity Manage permissions on code artifacts and Spark pools Manage — Triggers Overview It defines a unit of processing that determines when a pipeline execution needs to be kicked off. Manage — Integration runtimes Overview Integration runtimes are the compute infrastructure used by Pipelines to provide the data integration capabilities across different network environments.
An integration runtime provides the bridge between the activity and linked services. Azure Synapse Analytics Data Integration Net, etc. Pipelines Overview It provides ability to load data from storage account to desired linked service.
Load data by manual execution of pipeline or by orchestration Benefits Supports common loading patterns Fully parallel loading into data lake or SQL tables Graphical development experience Triggers Overview Triggers represent a unit of processing that determines when a pipeline execution needs to be kicked off.
Data Integration offers 3 trigger types as — 1. Schedule — gets fired at a schedule with information of start date, recurrence, end date 2. Event — gets fired on specified event 3. Tumbling window — gets fired at a periodic time interval from a specified start date, while retaining state It also provides ability to monitor pipeline runs and control trigger execution.
Manage — Linked Services Overview It defines the connection information needed for Pipeline to connect to external resources. Manage — Integration runtimes Overview It is the compute infrastructure used by Pipelines to provide the data integration capabilities across different network environments.
An integration runtime provides the bridge between the activity and linked Services. Person AS p ON e. Group by with rollup Creates a group for each combination of column expressions.
Overview Specifies that statements cannot read data that has been modified but not committed by other transactions. This prevents dirty reads. Locks are not used to protect the data from updates. Overview The JSON format enables representation of complex or hierarchical data structures in tables. OrderDate, OrderDetails. Promotes flexibility and modularity. Supports parameters and nesting. Overview Queries against tables with ordered columnstore segments can take advantage of improved segment elimination to drastically reduce the time needed to service a query.
Hash distributed Distributes table rows across the Compute nodes by using a deterministic hash function to assign each row to one distribution. Replicated Full copy of table accessible on each Compute node. Offers significant query performance enhancements where filtering on the partition key can eliminate unnecessary scans and eliminate IO.
Common table distribution methods Table Category Recommended Distribution Option Fact Use hash-distribution with clustered columnstore index. Performance improves because hashing enables the platform to localize certain operations within the node itself during query execution. If tables are too large to store on each Compute node, use hash-distributed. Staging Use round-robin for the staging table. The load with CTAS is faster.
Database Views Materialized Views Views Best in class price performance Interactive dashboarding with Materialized Views - Automatic data refresh and maintenance - Automatic query rewrites to improve performance - Built-in advisor Overview A materialized view pre-computes, stores, and maintains its data like a table.
Materialized views are automatically updated when data in underlying tables are changed. This is a synchronous operation that occurs as soon as the data is changed. The auto caching functionality allows Azure Synapse Analytics Query Optimizer to consider using indexed view even if the view is not referenced in the query.
No user action is required. Now, we add an indexed view to the data warehouse to increase the performance of the previous query.
A variable is an object that stores a single value. This value can be a string, a number or a date. The variable value is stored in Oracle Data Integrator. It can be used in several places in your projects, and its value can be updated at run-time. Knowledge Modules KMs are code templates. Each KM is dedicated to an individual task in the overall data integration process. The scenario code the language generated is frozen, and all subsequent modifications of the components which contributed to creating it will not change it in any way.
A Load Plan is an executable object in Oracle Data Integrator that can contain a hierarchy of steps that can be executed conditionally, in parallel or in series. Data validation is done before loaded to target table. No need to disable any constraint. A sequence is a variable automatically incremented when used. How to load valid records into one table and invalid records into another table? Stored procedures. Krishna Kiran, Dr. Rupa, Dr. S Avadhani Srinivas, Dr.
Krishna Rao, Ch. Madhava Rao, K. Anitha SaiRam, Dheeraj Srikanth, P Ranjana Venkatesh, Mrs. T Gowri Gayathri, R. Lakshmi Priya Srikanth N. Murthy J. Anitha M. Tech Ph. D Professor M. Data Warehousing is a single, unified enterprise data integration platform that allows companies and government organizations of all sizes to access, discover, and integrate data from virtually any business system, in any format, and deliver that data throughout the enterprise at any speed.
A mapping is a set of source and target definitions linked by transformation objects that define the rules for data transformation. Mappings represent the data flow between sources and targets.
They should be initially extracted from raw data of various data sources of systems, then after a serial of filtering and converting, and finally be loaded to data warehouse. This kind of process is defined as ETL process. Data Warehouse DW systems are used by decision makers to analyze the status and the development of an organization.
DWs are based on large amounts of data integrated from heterogeneous sources into multidimensional schemata which are optimized for data access in a way that comes natural to human analysts.
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