The World Customs Organization (WCO) recently conducted a BACUDA Data Analytics workshop for the Maldives Customs Service with 41 participants from the 30th of January to the 1st of February in Male, Maldives. The mission was financed by the Customs Cooperation Fund of Korea (CCF-Korea) and took place under the WCO's BACUDA initiative, the WCO capacity building project on Data Analytics. WCO experts and two BACUDA Scholarship graduates led the workshop. They delivered various sessions to equip the customs officials with the latest data analytics tools and techniques. One of the key highlights of the workshop was a hands-on session where the participants learned how to use Python language to work with the AI HS algorithm developed through the BACUDA project.

Do you have Data Warehousing, Hadoop/Data Lake experience? Do you like to solve the most complex and high scale (billions records) data challenges in the world today? Do you like to work on-site in a variety of business environments, leading teams through high impact projects that use the newest data analytic technologies? Would you like a career path that enables you to progress with the rapid adoption of cloud computing? This role will specifically focus on large scale data warehousing and data warehouse modernization.

Research and Markets estimated that annual global sales of information technology reached nearly $8.4 trillion in 2021. At that level, IT sales made up just less than 9% total estimated global annual gross domestic product (GDP). Global IT sales tend to grow about 6.6 percent annually. For the sake of argument, let's assume that the annual IT sales growth averages 6.6 percent from 2022 through 2030. This assumption includes global GDP growth for the period averaging just over 3.0 percent annually.

As more companies embrace the cloud, many are beginning to wonder what will happen to on-premises data warehouses in the years to come. Since the inception of data warehousing, the industry has witnessed many changes and advancements in terms of technology and infrastructure. However, to date, one thing remains constant -- the need for on-premises IT infrastructure to support the functioning of data warehousing. As cloud technologies continue to evolve and mature, we believe that the end of on-premises data warehousing is insight. This article looks at why this trend is in place and what will happen in the future.

Solutions Review's annual Vendors to Know in Data Management Solutions provides the details on the most critical solution providers in the space. The editors at Solutions Review continually research the most prominent and influential data management vendors to assist buyers in search of the tools befitting the needs of their organization. Choosing the right vendor and solution can be a complicated process; it requires constant market research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we listed the vendors to know in data management software. Note: Companies are listed in alphabetical order.

As known from many articles and publications, SAP offers three solutions for data warehousing. The SAP Business Warehouse (BW) was first published in 1997 and has therefore been a constant figure in the SAP Data Warehouse range for more than two decades. With HANA as a database platform, the HANA SQL Data Warehouse approach has been developing since 2015, which initially consisted of loosely coupled tools, but has since evolved into an open, yet highly integrated set of tools and methods, that can also be used to develop large data warehouse systems. Since 2019, the Data Warehouse Cloud has been completing the SAP solution as a SaaS solution. These three approaches are not in competition.

Data Warehousing – In today's flood of data, it is becoming increasingly difficult to maintain a clear data management system. More and more data sources are recorded via different software systems. A unified, centralized system can facilitate analysis and ensure that only one data truth exists in an organization. Data warehouse systems are built by integrating data from multiple heterogeneous sources and, in addition to centralization, performs the task of structuring data, supporting analytical reporting and structuring decision-making. The system can perform data cleansing as well as data integration and data consolidation and does not require transaction processing or recovery.

What do you mean by Data Warehousing? What do you mean by Data Mining?

One of the many technologies included under the umbrella of artificial intelligence, machine learning is defined by Wikipedia as "a field of computer science that gives computers the ability to learn without being explicitly programmed." The technology, which is a core part of the data analytics technologies that power the modern data warehouse, features algorithms that can make predictions on their own about data and its insights without being hampered by strict guidelines and instructions. When used successfully, machine learning can help with infrastructure scalability, cost savings, and agility. For its part, artificial intelligence (AI) is the ability of machines to think like humans. It stems from the idea that "given enough data and compute power, machines will be able to think and learn using mathematical simulation of the human brain," said John Santaferraro, research director at Enterprise Management Associates (EMA).

Multi-dimensional Time Series Analysis and OLAP methods are important, when working with Time Series Data. Often multi-dimensional Time Series Analysis as term is referred to is a complete set of methods in applying machine learning in forms of forecasts or searching for anomalies and patterns. In this article we focus on good old deterministic multi-dimensional Time Series Analysis foundations to prepare, investigate and aggregate the Time Series Data in a deterministic way. Knowing these multi-dimensional Time Series Analysis foundations is essential, because at least 80% of Data Science work is Big Data and Big Data Landscape preparation. Common multi-dimensional analysis operations get applied in Business Intelligence and Data Warehousing where they are often called Online AnaLytical Processing (OLAP) operations [1]. In this article, we discuss and describe what the most important multi-dimensional Time Series Analysis and OLAP methods are and show examples of how the different operations are applied on a Time Series Data sets. In the beginning, we talk about OLAP in Data Warehouse landscapes and Time Series Data processing in Big Data landscapes. Subsequently, we give some insights into why and to whom multi-dimensional time series analysis with OLAP matters within an enterprise.