Providing you with the resources, technology, expertise and best practices that will make your journey in the cloud a smooth one.
Over 20 years’ experience in Data Warehousing and Analytics to assist you migrate and implement Microsoft Azure solutions.
Our Azure Practice Team provides strategic advice and best practices for organisations seeking to leverage the cloud for Data and Analytics including Big Data and Machine Learning.
Our services include:
Based on your requirements, infrastructure and resourcing needs, we can help you design your solution architecture, taking into account any constraints around Data Volume, Data Latency, Fault Tolerance, Solution Availability and Security.
Our services include: analysis, design, development, testing, optimisation and handover. Typical solution delivery projects include:
Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. Using Azure Data Factory, you can create and schedule data-driven workflows (called pipelines) that can ingest data from disparate data stores. It can process and transform the data by using compute services such as Azure HDInsight Hadoop, Spark, Azure Data Lake Analytics and Azure Machine Learning.
A big data architecture is designed to handle the ingestion, processing and analysis of data that is too large or complex for traditional database systems. The threshold at which organisations enter into the big data realm differs, depending on the capabilities of the users and their tools. For some, it can mean hundreds of gigabytes of data, while for others it means hundreds of terabytes. As tools for working with big data sets advance, so does the meaning of big data. More and more, this term relates to the value you can extract from your data sets through advanced analytics, rather than strictly the size of the data, although in these cases they tend to be quite large.
Process data with U-SQL jobs in Azure Data Lake Analytics, using Hive, Pig, or custom Map/Reduce jobs in an HDInsight Hadoop cluster, or using Java, Scala, or Python programs in an HDInsight Spark cluster on a scalable Data Lake Storage.
Azure Stream Analytics provides a managed stream processing service based on perpetually running SQL queries that operate on unbounded streams. You can also use open source Apache streaming technologies like Storm and Spark Streaming in an HDInsight cluster.
For large-scale data exploration, you can use Microsoft R Server, either standalone or with Spark.
Leverage Microsoft Azure technologies to achieve both performance and scalability. Services like Data Lake(HDFS), Azure SQL Data Warehouse and Azure SQL Server provide flexible and scalable solutions to store your company data. Some of these services provide an easy migration path to follow as your data need and size grows.
Source, transform and load your data with tools Like Spark, Azure Data Factory v2 and SQL Server Integration Services.
Integrate your social feeds with services like Event Hubs and Stream Analytics or send and receive data to Internet of Things devices with IOT Hub.
Visualise your results with tools like Power BI or Microsoft SQL Server Reporting Services.
Using both iPaaS and ISaaS we cater for a variety of solutions to cover:
This approach is generally recommended in a lift and shift scenario where the solution hosted on Microsoft Azure needs to be very close to the legacy on-premises solution.
After your solution is live in a Production Environment, our trained and certified Managed Services team can provide support of your Microsoft Azure hosted solution. Support typically includes: