Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale.

Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs.

In this presentation, learn about the new products and features that make up Azure Synapse Analytics and how it fits in a modern data warehouse, as well as provide demonstrations.

James Serra is a big data and data warehousing solution architect at Microsoft. He is a thought leader in the use and application of Big Data and advanced analytics. Previously, James was an independent consultant working as a Data Warehouse/Business Intelligence architect and developer. He is a prior SQL Server MVP with over 35 years of IT experience. James is a popular blogger ( and speaker. He is the author of the book “Reporting with Microsoft SQL Server 2012”.

In today’s economy, financial services firms are forced to contend with heightened regulatory environments and a variety of market, economic and regulatory uncertainties.

Coupled with increasing demand from customers for more personalized experiences and a focus on sustainability/ESG, incumbent Banks, Insurers and Asset Managers are reaching the limits of where their current technology can take them with their Digital Transformation initiatives.

One of the most significant benefits provided by Databricks Delta is the ability to use z-ordering and dynamic file pruning to significantly reduce the amount of data that is retrieved from blob storage and therefore drastically improve query times.

Taking advantage of this approach over petabytes of geospatial data requires specific techniques, both in how the data is generated, and in designing the SQL queries to ensure that dynamic file pruning is included in the query plan.

This presentation demonstrates these optimizations on real world data, showing the pitfalls involved with the current implementation and the workarounds required, and the spectacular query performance that can be achieved when it works correctly.

The following is a guest post by Katherine Rundell.

1. Data annotation: what is it?

Data annotation is the process in which the raw data present in various formats such as text, video or images is labelled, in order to add vital information. In this day and age, machine learning is growing fast, and it needs such labelled data in order to understand input patterns properly. Without previously annotated data, all raw input is incomprehensible to any machine.

Data annotation is essential in creating machine-learning algorithms. When a machine is presented with data, they need to know exactly what to label, where and how, and they need to be trained for this process. One method of training is through human-annotated data sets. These are formed by running thousands of examples of correct data through the algorithm, and so training a machine to extrapolate all the rules and relationships behind the given data. The limits of a machine-learning algorithm are defined by the level of detail and accuracy of annotated datasets. Gary Olsen, AI blogger at UKWritings and Ukservicesreviews, says that there is a very strong relation between high-quality datasets and high-performance algorithms.

2. Types of data annotation

Data annotation can be found in various forms, which depend on the kind of datasets they are based on. By this classification, there can be text categorisation, image and video annotation, semantic annotation, or content categorisation.

Through text and content categorisation it is possible to split news articles into different categories, such as sports, international and politics. Semantic annotation is the process through which different concepts within a text are assigned labels, for example people names, company names or objects. Image and video processing is the task through which machines learn to understand the visual content which they are presented: it is also the task involved in recognizing and blocking sensible content online.

3. Entering data annotation

In general, AI models are built around certain tasks of entering data annotation, which can be split into four categories.

The first task is sequencing, which includes text or time series that have a start, an end and a label. An example of sequencing would be recognizing the name of a person in a large block of text. Another possible task is categorisation, for example categorising a certain image as offensive or not offensive.

Segmentation is another category, through which machine-learning algorithms find objects in an image, spaces between paragraphs, and even find the transition point between two different topics (for example, in a news broadcast). The last one is mapping, through which texts can be translated between languages, or be converted from full text to summary.

4. Data annotation services

Two of the most famous and efficient services involved with machine-learning are Amazon Mechanical Turk and Lionbridge AI.

Mechanical Turk, or MTurk, is a platform owned by Amazon, where workers are paid to complete human intelligence tasks, such as transcribing text or labelling images. The output of this platform is used to build training datasets for various models or machine learning.

Lionbridge AI is another platform for human-annotated data, written in 300 languages with over 500.000 contributors across. Jason Scott, tech writer at AustralianHelp and Simple Grad, states that through this platform, clients can send in raw data and instructions, or get custom staffing solutions for tasks with specific requirements, such as custom devices or safe locations.

5. About outsourcing

For companies, finding reliable annotators can be a difficult task, as there is a lot of labour involved in this, such as testing, onboarding or ensuring tax compliance to the distribution, management and assessment of projects.

Because of this, many tech companies often prefer to just outsource to other companies, which are known to specialise in data annotation. By doing this, they ensure that the process will be overlooked by experienced workers, and that they will use less time annotating data and more time on building search engines.

Search engines nowadays are becoming more and more efficient and technologically advanced. Even so, no problem can be solved through machine learning without having the necessary data. Data annotating ensures that search engines can function at their best capabilities, and a good dataset could potentially put newer search engines on the competitive market.

Author Bio

Katherine Rundell writes for Big Assignments and Top assignment writing services in New South Wales. She is an expert in machine learing and AI. Also, she teaches academic writing at Best Essay Services Reviews.

At its core, Arrow was designed for high-performance analytics and supports efficient analytic operations on modern hardware like CPUs and GPUs with lightning-fast data access.

It offers a standardized, language-agnostic specification for representing structured, table-like datasets in-memory.

Here’s a great overview.

To support cross-language development, Arrow currently works with popular languages such as C, Go, Java, JavaScript, Python, R, Ruby, among others. The project also includes a query engine that works with Arrow data called DataFusion that is written in Rust.