This document provides steps to create a hybrid provider based on a data store object (DSO) using real-time data acquisition in SAP BW 7.30. It involves creating a data source, info packages, transformations, DSO, hybrid provider, daemon, and process chain. Real-time data is extracted from the source system and loaded into the DSO. A daemon then loads the delta from the DSO into the hybrid provider's info cube for historic querying and reporting.
Table partitioning is a data organization scheme in which table data is divided across multiple storage objects called data partitions.
In SAP HANA database, it is possible to split column-store tables horizontally into disjunctive sub-tables or partitions. The SAP HANA database supports several redistribution operations that use complex algorithms to evaluate the current distribution and determine a better distribution depending on the situation. Partitioning is typically used in distributed systems, but it may also be beneficial for single-host systems. Partitioning is transparent for SQL queries and data manipulation language statements.
In a distributed SAP HANA system, tables are assigned to an index server on a particular host at their time of creation, but this assignment can be changed. In certain situations, it is even necessary.
In SAP HANA side-by-side implementation, SLT will stop replication when SAP HANA table reaches 2 billion records as a non-partitioned table cannot store more than 2 billion rows.
Advantages of partitioning:
+ Load balancing in a distributed system
+ Overcoming the size limitation of column-store tables
+ Parallelization
+ Partition pruning
+ Improved performance of the delta merge operation
+ Explicit partition handling
SAP HANA supports:
- Hash Partitioning
- Range Partitioning
- Round-robin Partitioning
How to free up memory in HANA? Is it possible to unload Table from memory?
- Yes, it is possible. SAP HANA Appliance can be used in smarter way to achieve maximum out of it.
In a typical business scenario, one SAP HANA appliance is used for DEV, TRN, TST environments. So, definitely there will be a major issue in this case – How to handle memory as memory is limited in that particular HANA appliance?
Under normal circumstances, the SAP HANA database manages the loading and unloading of tables into and from memory independently. Actually, the main aim of SAP HANA database is to keep all relevant data in memory.
But, one can manually load and unload individual tables if necessary. How?....It’s simple, and it can be done from SAP HANA Studio itself.
Select the table by right click and choose the option “Unload …”
And later, manually load the table into memory again, if needed.
Moreover, if somebody fires the query associated to the same unloaded table, SAP HANA will pick & load the same table into memory FULL or PARTIALLY, purely depending upon the executed query.
Again, if one needs to free up memory, he/she can manually trigger the delta merge operation for a column table manually in SAP HANA Studio. The delta merge operation is related to the memory management concept of the column store, i.e, the part of the SAP HANA Database that manages data organized in columns in memory.
So, options are as follows:
“Unload …” – Free up memory by unloading table from memory
“Load …” – Loading the table into memory
“Merge…” – Triggering delta merge operation for a column table
So what is SAP HANA? How can it help my area (Line of Business) and our business overall!. Presentation lays out BASICS and how can help users enable their area/business "Real time".
Table partitioning is a data organization scheme in which table data is divided across multiple storage objects called data partitions.
In SAP HANA database, it is possible to split column-store tables horizontally into disjunctive sub-tables or partitions. The SAP HANA database supports several redistribution operations that use complex algorithms to evaluate the current distribution and determine a better distribution depending on the situation. Partitioning is typically used in distributed systems, but it may also be beneficial for single-host systems. Partitioning is transparent for SQL queries and data manipulation language statements.
In a distributed SAP HANA system, tables are assigned to an index server on a particular host at their time of creation, but this assignment can be changed. In certain situations, it is even necessary.
In SAP HANA side-by-side implementation, SLT will stop replication when SAP HANA table reaches 2 billion records as a non-partitioned table cannot store more than 2 billion rows.
Advantages of partitioning:
+ Load balancing in a distributed system
+ Overcoming the size limitation of column-store tables
+ Parallelization
+ Partition pruning
+ Improved performance of the delta merge operation
+ Explicit partition handling
SAP HANA supports:
- Hash Partitioning
- Range Partitioning
- Round-robin Partitioning
How to free up memory in HANA? Is it possible to unload Table from memory?
- Yes, it is possible. SAP HANA Appliance can be used in smarter way to achieve maximum out of it.
In a typical business scenario, one SAP HANA appliance is used for DEV, TRN, TST environments. So, definitely there will be a major issue in this case – How to handle memory as memory is limited in that particular HANA appliance?
Under normal circumstances, the SAP HANA database manages the loading and unloading of tables into and from memory independently. Actually, the main aim of SAP HANA database is to keep all relevant data in memory.
But, one can manually load and unload individual tables if necessary. How?....It’s simple, and it can be done from SAP HANA Studio itself.
Select the table by right click and choose the option “Unload …”
And later, manually load the table into memory again, if needed.
Moreover, if somebody fires the query associated to the same unloaded table, SAP HANA will pick & load the same table into memory FULL or PARTIALLY, purely depending upon the executed query.
Again, if one needs to free up memory, he/she can manually trigger the delta merge operation for a column table manually in SAP HANA Studio. The delta merge operation is related to the memory management concept of the column store, i.e, the part of the SAP HANA Database that manages data organized in columns in memory.
So, options are as follows:
“Unload …” – Free up memory by unloading table from memory
“Load …” – Loading the table into memory
“Merge…” – Triggering delta merge operation for a column table
So what is SAP HANA? How can it help my area (Line of Business) and our business overall!. Presentation lays out BASICS and how can help users enable their area/business "Real time".
Harnessing the power of the Web to Reinvent Management.
The Management 2.0 Hackathon, a joint collaborative effort by the MIX, Saba, and the Enterprise 2.0 Conference, was inspired by hacakathons in the world of software development. A management hackathon is a short, intense, coordinated effort to develop useful hacks—innovative ideas or solutions—that can be implemented by organizations to overcome barriers to progress and innovation.
For the Management 2.0 Hackathon, we wanted to discover what pathologies were holding backing Management 1.0 today, what principles of the Web could inspire Management 2.0, and where companies are already applying these principles successfully. The process would culminate in the development of management hacks, designed to be practical experiments and practices that any organization could apply today.
More than 900 progressive management practitioners and technologists from around the world joined this hands-on effort—sharing perspectives, contributing ideas, and generating hacks.
It was a massive collaborative effort that yielded some very compelling results.
Refer to: http://www.managementexchange.com/blog/management-20-hackathon-using-inspiration-web-hack-management
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Big data insights with Red Hat JBoss Data VirtualizationKenneth Peeples
You’re hearing a lot about big data these days. And big data and the technologies that store and process it, like Hadoop, aren’t just new data silos. You might be looking to integrate big data with existing enterprise information systems to gain better understanding of your business. You want to take informed action.
During this session, we’ll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data. You’ll learn how Red Hat JBoss Data Virtualization:
Can help you integrate your existing and growing data infrastructure.
Integrates big data with your existing enterprise data infrastructure.
Lets non-technical users access big data result sets.
We’ll also provide typical uses cases and examples and a demonstration of the integration of Hadoop sentiment analysis with sales data.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Couchbase Chennai Meetup 2 - Big Data & AnalyticsRedBlackTree
A set of case studies on Big Data and Analytics using Couchbase. This was from a presentation by Kadhambari Anbalagan, Architect at RedBlackTree, at the 2nd Couchbase Chennai meetup.
The slide deck for the Power Platform Presentation in SQL Saturday Redmond 2019. We have reviewed the power Platform Components, why is it better together and how to make it happen. During the demo all the options of implementation between the Power Apps and PowerBI were demonstrated. Including the data visualization changes with new data feed. Use some of the following ideas in your organization and POC's for more complex implementations.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One was is to first persist the data into a data store and then use a traditional data visualisation solution to present the data.
If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and highlights some of the products available to implement these blueprints.
Similar to Hybrid provider based on dso using real time data acquisition in sap bw 7.30 (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.