In this article, we will look at the two leading data and cloud computing technologies offered by the biggest names in technology. Azure SQL and AWS Redshift are two technologies which are popular when architecting data warehouses. Ardent’s engineers are proficient in both have delivered several data warehousing projects for clients using these leading technologies.

Data warehousing with Azure SQL

Azure SQL is a managed Data Warehouse-as-a Service (DWaaS) provided by Microsoft. It acts as a federated repository for data, which is collected by a business’s operational systems. The architecture of the data warehouse is based on the latest general release of the SQL server, and can be used by data analysts, data scientists, and end-users to run queries, process information, and examine metric data.

Pros and Cons of Azure SQL

There are a number of benefits and limitations of using Azure SQL for warehousing your data. Benefits:

• Scalable computing power
• Full compliance with PCI-DSS, SOX, and HIPAA standards/regulations
• Built-in Threat Detection
• Makes use of Azure cloud compute and storage resources
• Microsoft is a trusted brand for system management
• Suitable for enterprise-level data warehousing

Limitations

• Moving data into the cloud can be difficult
• Some functions are considered outdated and only work through the ‘Classic’ portal
• Only supports up to 32 connections at a time

Read about Ardent being a Microsoft Gold Partner and what it means for you.

Data warehousing with AWS Redshift

Redshift is an Amazon product that provides a petabyte (PB) scale data warehouse service that operates with cloud technology. The warehouse service is fully managed and can be scaled to allow growth for new insights and customers. Based on PostgreSQL, the platform offers a high level of flexibility and can integrate with most third-party platforms with its JDBC and ODBC drivers.

Pros and Cons of Redshift

Each technology has a number of key benefits and some limitations you should consider.
Benefits:

• High-performance standards
• Flexible scalability and room for data growth
• Wide selection of easily administered tools for automation
• Widely supported querying tools
• Offers columnar storage
• RBAC encryption and SSL connections
• HIPAA, SOC2 Type II, Fed ramp, and PCI certified

Limitations

• Does not provide a multi-cloud solution
• Concurrent execution can cause performance problems
• Is not based on a serverless architecture

Read about Ardent being a certified AWS partner and what that means for you.

Key differences between Azure SQL and Redshift for data warehousing

When looking at Azure SQL and AWS Redshift for data warehousing, your specific data needs, database set-up, operational requirements, and familiarity with the platforms, will largely influence how you make your decision.

Scalability

There are lots of elements to consider, for example, when it comes to scalability, Redshift cluster modification is done through a management console or an API, with the changes immediately applied. With the Azure SQL data warehouse, scaling of clusters can be done through compute and storage units independently and can take minutes to be applied.

Indexing

For Indexes, Azure supports all SQL concepts (indexes, stored procedures, user-defined functions), whereas Redshift supports two kinds of sort keys (compound and interleaved).

The differences between the two platforms are largely in functionality and operation, which can make deciding a challenge if you are not familiar with the intricacies involved, and seeking expert advice and assistance is highly recommended.

Ardent data warehousing services

Ardent engineers work with a number of world-leading technologies to deliver robust, highly scalable and accessible data warehouses for our clients. Azure SQL and AWS Redshift are just some of the technologies we have employed for data warehousing. If you are looking to build a data warehouse and are unsure of the right data warehousing technology stack for your business, data and objectives, we can help. Let’s set up a quick discovery call to discuss your challenges to finding a data warehousing solution that is right for you.

Explore our data warehousing services or our data engineering services.

The post Data Warehousing Architecture: Azure SQL vs Redshift appeared first on Ardent.

Being able to easily access your data, sort it in a manner that makes sense for your business, and know that it is protected in case of power disruptions or cyber attack are all business-critical considerations that must be addressed when determining the best data solutions. We’re looking at data warehouse services Vs databases – what they each do, what the pros and cons of the technologies are, and how they differ from one another, so you can make informed decisions to benefit your business.

What do you need to know about Data Warehouses?

A data warehouse is essentially a central repository for your information to be stored and accessed. Data warehousing services provide space and the tools to integrate data from various sources and create a searchable database. Depending on the service, the data warehouse may also have additional functions for data source integration, lifecycle management, data modelling, and other advanced organisational options.

The pros and cons of a Data Warehouse

As with many other technologies and platforms, there are both reasons for and against using a data warehouse, and these should be carefully considered before a structure is developed and put in place.

The benefits of a data warehouse include:

• Easier integration of data from multiple sources
• Error identification and suggested corrections
• Faster data retrieval

Some limitations to consider:

• Higher maintenance costs
• Increased time spent in preparing the data for the warehouse
• Issues with compatibility, especially at the initial set-up stages

Read about data warehouse Vs data lake and choosing the right data solution.

What do you need to know about Databases?

A database is a structured set of data, which is generally held in an individual location, that can generally be accessed in various ways.

There are different types of databases which are used depending on the project’s needs. The main types of databases are:

• Relational
• Object-Orientated
• Hierarchical
• Network

The Pros and Cons of a Database

Making use of an internal or ‘local’ database can have benefits and problems associated with it.

Some of the pros include:

• Minimised data inconsistency
• Stricter control over data redundancy
• Ease of sharing

But some of the cons to consider include:

• Additional costs involved with staff training
• Hardware and software updates and maintenance costs
• Data conversion may be costly
• Incompatibility with other software

Data Warehouse vs Database and the key differences

The key difference between using a local database and a data warehouse service is accessibility – with data warehouses offering wider access.

However, it’s also important to consider that many data warehouses have easy options to purchase more space when the database grows in size, an internal database is limited to the size restrictions of the local server, and it can be both costly and time-inefficient to upgrade the server when these limits are reached. Making the correct choice will rely on your specific business needs, the size of the team involved in working with the databases (or accessing them), and whether you have a growth and expansion plan to consider.

Ardent data warehousing service

Ardent has worked with a wide variety of clients to provide a data warehousing service to ensure data organisation and accessibility. If you are dealing with slow query performance, data coming from multiple sources and increasing load to data, get in touch to find out how Ardent can help you effectively store your data. Our engineers help our clients unlock their data potential every day and can do the same for you.

Explore our data engineering services

The post Data Warehouse vs Database: The key differences appeared first on Ardent.

What are data pipelines

A data pipeline is a series of processing steps that the data will go through from the source which can be a software, system or a tool to a destination which can be a data warehouse, data lake or other another data storage structure. Data pipelines will collect and collate data from disparate sources and process it for efficient storage and analysis. Data pipeline development, when done right can offer multiple benefits to organisations from ensuring data is clean to enabling end users to gain useful, meaningful insights.

Why you need a pipeline for your data

If you have data that is sat across a wide variety of sources and systems that are not connected, then you are not accessing the full potential of your data that could provide you with meaningful insights. These insights can inform better decision-making and support any monetisation efforts if you are looking to sell data and insights to clients. For example, we worked with one of our market research clients to build a pipeline that would collect data from their data storage and feed it through to the end clients’ data analytics and reporting tool.

Key pipeline challenges

There are many challenges organisations can face with data pipelines. Data pipelines that are poorly architected or are not built with scalability in mind can be challenging to deal with. Here are some key challenges you might find with data pipelines:

• Growing data – Growing data can cause challenges with your data pipelines if they were not developed to handle large volumes of data.
• Growing data sources – Collating data from multiple sources can be challenging, however, the more data sources added to the pipeline the more complex they become.
• Data security – Data security should be a priority for any organisation, especially those that deal with sensitive or consumer data. Collating and collecting data from a disparate number of sources can increase the risk. However, ensuring you are following best practices can mitigate any risks.
• Managing pipelines manually – Manual effort on any process can drain productivity and require a lot more time and resources. Building pipelines with automation in mind can save you time and resources in the long term.
• Variety of data without standardization – Data that is varied in size, format and structure can be difficult to organize and standardize. Therefore, this data will need to be mapped in a common format to ensure that you can obtain the insights you require.

Read the full article on key challenges with managing data pipelines.

Key benefits from pipelines

Having efficient, effective pipelines with automation can provide a wealth of benefits to organisations. These are some of the key benefits of data pipelines:

• Centralized data to improve data visibility
• Uncover compelling insights to drive data-driven decision making
• Improve data security with data visibility
• Create an efficient data structure to mitigate issues and resolve them quickly if any arise
• Forecast and make predictions based on your data
• Get the full picture with unified data

Data pipeline development technologies

There are several world-leading technologies you can employ to build your data pipelines. At Ardent, our engineers work with multiple technologies including the likes of AWS Redshift, AWS S3, AWS DynamoDB, Apache Hadoop, Apache Spark, Python and much more. Choosing the right technologies and platforms to build your data pipelines with will depend on a number of factors including:

• Data – The volume, variety and velocity of data you are dealing with as well as the number of data sources.
• Budget – Most popular tools and platforms will have costs associated with them whether that’s licensing costs or finding individuals with the right skill set and training in those technologies.
• Scalability – Is scalability a key requirement for you? It should be for most instances, in which case you will need to choose technologies that will allow you to scale your data pipelines.
• Latency – How quickly do you need to be able to access your data and have it ready for analysis and reporting? If you are looking at real-time latency then you will need to select technologies that will allow that.

Find out about our technology partners.

Types of data pipelines

There are four most popular types of data pipelines which include batch data pipelines, ETL data pipelines, ELT data pipelines, and real-time data pipelines. We will explore each of these below.

ETL data pipelines

ETL is the most common type of data pipeline that has been the main structure of data pipelines for decades. The structure of the ETL data pipeline is extract, transform and load. The data is extracted from disparate sources, it is transformed through the processes of cleansing, validation and enrichment to match it to a pre-defined format and loaded into the data storage infrastructure whether that is a data warehouse, database, data mart or a data lake.

Read the success story on ETL pipeline development with AWS infrastructure.

ELT data pipelines

The ELT structure is a more recent type of data pipeline which follows the structure of extract, load and transform. This is a more flexible approach when it comes to dealing with data that will vary over time as it is extracted, loaded and then transformed. The data is extracted from multiple sources (same as the ETL structure), it is then loaded directly into a data storage infrastructure (data warehouse, database, data lake or a data mart) and then it is formatted in line with the end requirements. ELT is more suitable for organisations that may use the data for multiple different purposes. As it is a relatively new structure, it can be difficult to find experts in this type of data pipeline development.

Batch pipelines

Batch pipelines focus on processing data in set blocks (batches) hence the name. This makes the processing of large volumes of data quicker and more efficient. This type of processing is typically carried out during down times such as evenings, nights and weekends when the systems are not all fully in use. Batch pipelines are for those organisations that may want to collect all historic data to make data-driven decisions, a great example of this is market research companies collecting survey data. Batch processing can take a few minutes, hours or even days depending on the quantity of data.

Read our client success story on batch pipeline development.

Real-time data pipelines

Real-time data pipelines are those that process the data in real time and make it available and accessible for data reporting and analysis instantly. This can be a complex and challenging process, especially when dealing with large volumes of data coming in at varying speeds. Real-time data pipelines are suitable for organisations that want to process data from streaming locations such as financial markets. There is an increasing demand for real-time analytics therefore, it can be expected that real-time data pipelines will become more prominent in the upcoming years.

Read our client success story involving real-time data processing.

Data pipeline automation

Automation of any process offers invaluable benefits from improved productivity and removal of human error to streamlined and efficient processes. This is no different to automating data pipelines. There are several key benefits of automating data pipelines and they include:

• Save time and resources – You can save significant time and resources and consequently costs as you automate your data pipeline processes.
• Streamlined data pipelines – With automation, you streamline the entire process and remove the need for manual coding.
• Improve turnaround – Improve data accessibility and turnaround time for speedier data analysis and reporting.

Outsourcing data pipeline development

Building data pipelines in-house can be a good idea if you have the in-house resource, experience, skills and expertise. However, if you do not, then it might be worth considering outsourcing data pipeline development. Here is why you might consider the outsourcing approach:

• Work with experienced engineers – When you outsource your data pipeline development you will be working with experienced engineers that have gained invaluable knowledge which can ensure your data pipelines are built to meet your requirements.
• Access highly skilled and trained professionals – You can access professionals that have specific training and skills in building pipelines without having to go through lengthy and costly recruitment processes.
• Avoid licencing costs for technologies and platforms – When you are working with a third party, they will cover the costs of technology licensing as they are more than likely to have existing licensing in place. This again saves you costs in comparison to carrying out the project in-house.
• Costs effective – If you decide to opt for the in-house route without the right skills you may face complex challenges which may lead to costly errors. Therefore, investing in the right data engineering company will save you long-term costs.

Data pipeline success story

For a leading market research client, our data engineers architected robust, scalable data pipelines with AWS infrastructure to ingest data from multiple sources, cleaned it and enriched it. The speed processing of data was a challenge as it was considerably large volumes of data. However, our data engineering employed EMP (Elastic MapReduce) to significantly reduce the processing time.

Read the full story here: Powerful insights driving growth for global brands

Ardent data pipelines development services

Ardent’s highly experienced data engineers have worked on a number of projects building robust, scalable data pipelines with built-in automation to ensure a smooth flow of data with minimal manual input. Our teams work with you closely to understand your business challenges, your desired outcomes and your end goal and objectives to build data pipelines that will fulfil your unique needs and requirements. Whether you are dealing with data that is spread across disparate sources or have constant large volumes of data coming in, we can help, get in touch to find out more or to get started.

Explore our data engineering services or our data pipeline development services.

The post Building data pipelines – a starting guide appeared first on Ardent.

Over time, as you add more data, or change the setup of your SQL database, the schema can get unnecessarily complicated, and the performance of the database begins to drop. Optimising your SQL server schema is a necessary part of your data management strategy to ensure you are making the best use of your space and have data solutions that run correctly.

What are the benefits of optimising your SQL server schema?

The optimisation process aims to define the most efficient techniques and schema layout. Amongst other things, it can be used to improve query performance, the best use of system resources, and allow performance metrics to deliver results that match the full capabilities of your setup.

SQL performance is affected by table sizes, and the more data you have to be searched through, the more likely it is that your query will be slower, and when you’re using a service which operates on a pay-as-you-use basis (such as Microsoft’s Azure), these fractions of time add up to a waste of budget, and your time.

The technology for SQL schema optimisation

Most cloud platforms will have tools and automated options that provide data optimisation, but there are also options for manual evaluations, detection of performance problems, and monitoring metrics to evaluate runtimes. Some simple optimisation techniques to practice, and things to be aware of include:

• Using INT to store integers instead of strings
• Using Date type, instead of storing dates as string
• NULL columns can be harder to optimise (they need more space and require special processing)
• For primary and foreign keys, choose one datatype and keep it in all tables

What to consider when choosing to optimise your SQL server schema

Before getting started with any critical systems or data evaluations and changes, always check when the last backup snapshot of the data was taken and create a new one (if necessary), so you have a roll-back point should things go wrong.

If the platform you are using has analytic tools and predetermined metric evaluation, it is a good idea to run these first, or undertake an evaluation manually; this allows you to see exactly how your schema is performing and identify areas for optimisation. Then, you can either run the schema optimisation tools or begin your manual adjustments.

When and how you decide to optimise your schema will depend on how often you’re changing or adding to it, and what additions have been made. It is good practice to run the optimisation tools or process on a regularly scheduled basis, to keep your database working properly, and ensure the SQL database can function at its most efficient.

Optimise SQL server schema with Ardent

Ardent data engineers have worked with clients across industries to help optimise schema design for several clients. The optimisation of a schema ensures your data is performing as it should be and is scalable to meet increasing and evolving data sets. If you are facing limitations with your data which are hindering your growth and the fulfilment of your vision, then get in touch to find out how Ardent data engineers can help.

The post Optimising SQL server schema – what you need to know appeared first on Ardent.

In this article, we will look at migrating data and how to effectively plan your data migration so you can avoid failure, additional spending and prolonging the project with insights from Ardent experts.

Data migration plan

Goals and objectives

You will have the goals and objectives of your migration project and how they will contribute to your business growth. However, establishing these and having them as part of your plan will help you keep on track. Write down the reasons for migrating your data along with the key challenges it will help you solve. Has your data outgrown your existing infrastructure, do you have challenges with data performance or accessibility? Whatever the reason, it is important to capture and keep the end goal in mind duration of the migration project.

Data migration strategies

There are two main data migration strategies you can employ for your data migration project, whether that is ‘Trickle database migration’ or a ‘Big bang database migration’. Both have key benefits and limitations to consider, therefore you will need to find the right solution for you.

Trickle database migration – Trickle database migration is the migration of data in small segments. This means that each segment of data will have its own scope and timeline and it will ensure that your data migration is more manageable. This approach can be costly as you will have to run both systems in synchronisation, however, this does mean there will be zero downtime and you have better data accessibility.

Big bang database migration – The big band database migration can be a cost-effective solution, however, there is an increased risk of failure as all the data is migrated at one time. This also requires a downtime of the systems with a lack of data accessibility during that time.

Find out more about data migration strategies and choosing the right one for you.

Data migration technologies

If you carry out a lot of your data engineering in-house whether that is managing your database or monitoring your data warehouse, then you will likely have a preferred technology stack. Therefore, you may opt for similar technologies. For example, if you have always worked with AWS technologies, then you may choose to adopt Redshift for your data migration as you are familiar with AWS. Alternatively, you may not find AWS technologies suited to you or your business, whether that is cost complexity or another reason altogether. Instead, you may opt to employ Azure, Ms SQL Server or Databricks to carry out your data migration.

If you are finding it a challenge to decide on the right technologies, as your decision will have a long-lasting impact, whether that’s positive or negative, get in touch with an expert.

Find out about our AWS expertise and being a Microsoft Gold Partner.

Risk assessment

Carrying out a risk assessment is imperative in order to mitigate any costly issues that may arise. Note down all the potential risks that may arise. For example, you may think your data sets might have compatibility issues with the new system or technologies. Therefore, to mitigate this, you can run tests to see if your data can perform within the new infrastructure. Another challenge you may face is facing issues with data quality if you are migrating all your data without cleansing it. Therefore, it might be worth cleansing your data or initiating some data governance practices to ensure your data is clean, accessible and useful.

Budget

The budget may be out of your control, however, once you have a budget for the project, you can work on how you will approach your data migration. If you have in-house resources you can utilise them it might be a cost-effective solution to carry out the data migration in-house. However, if you have a budget that can cover outsourcing your data migration, then it’s an option you should consider as it can save you long-term costs as you will be dealing with experienced data engineers.

Timeline of the project

Create a project timeline and set achievable goals and objectives within a certain time frame. If you are going with the trickle data migration option, then you can set KPIs to measure the success of each mini data migration you carry out. Creating a timeline will help you understand realistically what milestones you can achieve within a set time frame. Additionally, you may want to share this timeline with all those involved to provide a rough time frame of when they may expect any downtime. It can also help to ensure the entire team is on the same page of working together towards the end goal.

Testing

The end of data migration is not once the data has been moved from one location to another. Testing needs to be part of the entire project to ensure that no issues and failure occurs with the data in the new environment. It’s essential to validate the data layer as part of the data migration project.

Outsourcing data migration  

Once you have created your data migration plan, you may find that you require expert assistance to bring your data migration project to completion. You may want to consider outsourcing your data migration as it will offer you access to experienced data engineers, leading technologies and peace of mind knowing your data is being handled by experts.

If you do opt for outsourcing your data migration, there are many factors you should consider for your data migration provider including:

• Successful projects and proven track record
• Relevant experience
• Knowledge and access of leading technologies
• Data security practices and accreditations

Ardent data migration solutions

Ardent have a proven track record of successfully delivering data migration solutions to a wide variety of clients dealing with a range of data. Our data migration engineers are proficient in leading technologies with expertise in AWS, Microsoft (Azure), Oracle, Databricks and more. If you are looking to migrate your data by experienced engineers, then get in touch to find out how Ardent can help you achieve your data migration goals.

Explore our data engineering services and our technology partners.  

The post Migrating data – how to plan your data migration appeared first on Ardent.

Data migration, if delivered effectively can provide a wealth of benefits including streamlined data, improved database performance and optimised data accessibility. Here, we will explore the key factors to take into consideration for your data migration project.

Identifying your data for data migration

Identifying the data that you want to migrate is key to getting started. You need to establish the data source, data type and format and data sensitivity. If you are moving a variety of data then it might be a good idea to separate the data and mark it with the required information, therefore sour and location, type and format and rank in terms of sensitivity. Establishing this information will help you feel well informed on getting started with the project. It can also help you find any gaps or any other data that may also need to be migrated.

This is the key stage before you get started on creating a data migration plan for your project.

Creating a data migration plan

According to Gartner around 83% of data migration projects fail or exceed their deadlines and budgets. Therefore, planning is crucial. Data migration can be complex, therefore ensuring you have a solid plan at hand can make or break your project. A migration plan will give you a solid foundation to work from during the project. Some things you should consider having in your plan include:

• Goals and objectives – any good plan will highlight the goals and objectives of the project. What are you looking to achieve? Reduced long-term costs, improve data performance? It may also be worth adding KPIs to measure progress.
• Risk assessment – what are the potential risks and what are the actions you are going to take to mitigate them?
• Identifying support – If you need support where can you find support, this could be an external party or a data partner.
• Project duration – Set a project duration with realistic goals and deadlines.
• Identify those affected – the migration of the company’s data may impact several individuals in the organisation, therefore, highlight who needs to be made aware so they are prepared for any system dropouts.
• Selecting technology – What tools and software will you be employing for your migration plan and why? Are they cost-efficient? Have you used them previously? Do they suit your type of data, needs and requirements?
• Ability and resource – Do you have the ability, skills, expertise and resource available for the migration project? There are a lot of businesses that fail with their data migration because they underestimate the scope of the project.

Ensuring backing up for all data

To avoid any data loss, we recommend you ensure all your data is backed up should there be any issues or errors during the data migration process. Our data engineers have worked on many data migration projects and ensure every time that the data is back up. Data back should be an essential practice if your company deals with big data, however, it is incredibly important for the migration process. Backing up the data on multiple platforms is preferable, although it might not always be viable. This is why you should at least back up your data on a cloud platform which differs from the main location of your data store.

Read more on hybrid-cloud or multi-cloud and choosing what is right for you.

If you have large volumes of data then it can be a hugely time-consuming process, therefore you may want to carry this out during less busy times. For example, if your team are working during the week, consider backing data on the weekends or outside of working hours. At Ardent, we carry out data backups for clients all across the world in line with when it is their downtime when possible.

Choosing the right data migration technology

Selecting the right tools and software for your data migration is key. There is a number of factors you should consider when selecting the right technology and these includes:

• Expertise and knowledge required – do you have the required expertise and knowledge in-house?
• Cost-efficient – Are they affordable to your business?
• Support – The larger companies such as AWS and Microsoft usually offer great support.
• Pros and cons – Weigh the benefits and limitations to find the right tech stack for your migration.

Ardent has helped a number of our clients choose the right technology stack for their data migration to suit their data, requirements and end goals. Our data engineers have decades of experience in successfully delivering data migration.

Outsourcing data migration

If you find the data migration process overwhelming and have found that you may not have the right skills and expertise in-house then you may consider outsourcing data migration. If you are looking to do this, then carrying out due diligence is key. Here are some questions may consider asking any potential partner:

• What relevant experience do they have in terms of data similar to your data, data migration project etc?
• What expertise do they have in specific technologies or are they able to make better recommendations?
• What data security practices are followed? For example, Ardent is an ISO 27001 certified company.

Ardent has been the provider of choice when it has come to outsourcing data migration for a variety of organisations from global leaders to local governments.

Stringent testing of the migrated data

Once the data migration process is complete and you have finished with the migration, you need to ensure stringent testing is carried out whether that is in-house or externally. This will help spot any issues or errors in data including corrupt or missing data or any there is there a presence of misformatted or incorrect data.

Regular maintenance

Carrying our regular maintenance checks on your data will ensure data system sustainability and help you mitigate the risk of any problems arising. We recommend that these maintenance checks are carried out regularly, especially if you’re data is not monitored and supported around the clocks.

Ardent data migration solutions

At ardent, our data engineers have worked with a multitude of companies delivering data migration solutions dealing with a variety of data, volume of data and varying data location and destination. If you are looking for an experienced data engineering partner with a proven track record in successfully delivering data migration solutions, get in touch to find out how we can help.

The post What to consider when migrating your data appeared first on Ardent.

Understanding your data could save you significant costs. According to Tech Jury, Netflix has saved over $1 billion by using their data to retain customers. If you are looking to make savings, drive growth and make data-driven decisions, you may want to outsource a data engineering partner to help you achieve your data goals. In this article, we will explore things you need to consider when looking for a data engineering partner.

Track record of successful data projects

One of the first things you should consider when looking to work with a data engineering partner for whatever service, whether that’s data warehousing, data management or data migration is the experience. Ensure that the data engineering provider has completed successful projects with the service you require. If you are in conversation, you can ask about successful projects, however, you may initially want to scope out the website and look into whether they have client success stories or client testimonials, this should give you a good indicator of whether a company will be a good fit for you.

Explore our client success stories

Relevant data engineering skills

The data industry is booming, however, there is a considerable skills gap as highlighted by Towards Data Science, which means it can be difficult to recruit for positions in data engineering. Outsourcing is the best alternative to this issue. If you are looking to outsource a data engineering provider, you need to ensure that they have the right skills to deliver your service. Do they have data engineers that have certifications in leading technologies? Do they have experienced data engineers on their team that are experts in their field? Additionally, you may also want to find out if they have expertise and skills working on projects with other businesses like yours, whether that’s by industry or the type of data you are dealing with.

A data engineering services provider with highly skilled people will be able to prove that they can successfully complete your project.

Expertise in data technologies

Firstly, you will need to consider which technologies you want to employ for your project. A good data technology partner will help you explore your options, whether that’s in line with your expertise in-house or if you require recommendations. Using the right technologies is key for your data, especially in the long term as it can impact the ease of use, maintenance costs and much more.

At Ardent, we are happy to make recommendations of the most suitable technologies for each project and client. Where one technology may be great for a client, another may fall short. As our engineers have used a wide variety of technologies to deliver our services, we are in the position to help you make your decisions.

Keeping your data secure

Keeping your data secure has never been so important, with cyberattacks increasing consistently on a year-on-year base. According to Forbes, in 2021 there was an increase of 15.1% in cyberattacks compared to 2020. When choosing a company to deliver your data services you need to ensure that they have robust data security practices in place to make sure that your data is safe.

At Ardent, we are an ISO 27001 certified company, which gives our clients peace of mind that we follow industry best practices to ensure their data is safe. Also, our data engineers work with world-class technologies that make data security a priority.

Effective communication throughout the project

Whether it’s short or long-term, effective communication is key for the project to be a success. Find out how the data engineering communicated by looking at channels of communication, the regularity of communication and updates and how they take on board feedback. This will ensure that you have a smooth communication process during the project, and you will be able to work together to meet the end goals.

At Ardent, we have a structured project delivery process, with regular updates provided to our clients and set, clear channels of communication.

Ardent, your data engineering partner

We have worked with a wide variety of clients across the world from a range of industries to help them achieve their data goals. We have the experience, the skills, the technologies, effective communication with robust data security to fulfil your data needs. If you are looking to work with a credible, reliable data engineering partner with a proven track record of success, get in touch to find out how we can help.

The post Things to consider when looking for a data engineering partner appeared first on Ardent.

As highlighted on Database Trend and Applications, around 93% of businesses in the UK and US say that improvements are required in how they collect, manage, store and analyse data. Here are the key challenges with data warehousing whether you have an existing data warehouse or if you are looking to build one and how you can overcome them, with insights from our Ardent data engineering experts.

Traditional data warehouses

A traditional data warehouse is a data warehouse which deals with on-premise server data. Although, these are not as common since the massive boom in cloud data warehousing they are still prevalent. Traditional data warehouses can be costly to maintain, lack speed and agility and have high failure rates. Therefore, organisations should look to adopt cloud data warehousing which offers a great number of benefits.

Read about hybrid-cloud and multi-cloud environments.

Time, cost and effort

If you are looking to start a data warehousing project, whether that is moving away from a traditional, on-premise data warehouse to creating a new data warehouse on the cloud you need to consider that it will require substantial time, cost and effort. This is something that businesses always struggle with when it comes to successfully building a data warehouse.

Here is how you overcome each challenge:

Time – Planning is key when it comes to predicting the time required. Outline key stages of the data warehousing development whether you are building it in-house or outsourcing data warehousing. Ensure that you have forecasted an accurate amount of time needed.

Cost – Find the best solution for you and your business. Most organisations will not have the resources in-house to build a data warehouse that will effectively improve performance, create consistency and optimise your data structure. Therefore, they will look for a third-party provider. Carry out your due diligence in finding a data engineering partner that will deliver the best value with the right experience and technology stack.

Effort – The process of planning, building and maintaining a data warehouse will require significant effort depending on how involved you are in the process. If you are working with an external partner, make sure to agree on how much time will be required from you and your business. This can help you better manage your time through the duration of the project

Expertise and skillset

As highlighted on Data Science Central, around 80% of data warehousing projects fail to achieve their aims. When it comes to achieving your goals you need to ensure that you have the right team to help you achieve your set goals. Finding the right skill set can be challenging. This is when you might want to consider outsourcing your data warehouse development. Make sure to work with data warehouse architects that have the experience, expertise and skill set to build a data warehouse that is built to help you achieve your data goals in line with your overall organisation objectives.

Overcoming data warehousing challenges

Well-architected data warehouses can provide countless benefits for organisations. Therefore, it’s crucial to ensure that you are taking the right steps to ensure that your data warehouse performs at optimum levels.

Ardent data warehousing services

If you are looking to update your current data warehouse, build a new one or migrate your data from one data warehouse to other, Ardent can help. Our highly skilled engineers have the skills, expertise and experience to help you unlock your data potential with our data warehousing services most suited to your data and data needs.

Explore all our data engineering services.

The post Key challenges with data warehousing appeared first on Ardent.

Did you know that we are a two times Microsoft Gold Partner? We have achieved a Gold Certified partnership with Microsoft in Application Development and Application Integration. It sounds impressive, but it is so much more than that.

What is a Microsoft gold partner?

A Microsoft Gold Partner is accreditation or status that organisations acquire once they fulfil the requirements and criteria set by Microsoft. The gold is the most prestigious of the partnerships, with there be three levels, regular partner, silver partner and gold partner.

What does it mean to be a Microsoft gold partner?

A gold Microsoft partner certified is an organisation that has shown consistent commitment to delivering excellence with Microsoft technologies. At Ardent, we continuously invest in a wide range of leading Microsoft technologies to bring innovation to the forefront of our client’s organisations. At Ardent, we ensure our engineers are committed to ongoing training and development to expand their skillset and expertise.

More on our technology partners.

Who can be a Microsoft gold partner?

Any company that provides Microsoft related products and services can be a partner as long as they meet the set criteria. These companies are usually technology companies ranging from those like Ardent providing software and data solutions to IT Training Centres.

What do you have to do to be a certified Microsoft gold partner?

To be able to acquire the Microsoft gold competency, you have to fulfil the criteria for the particular competency you want to be certified for. We will look at the requirements for both Application Development and Application Integration.

To become gold certified Microsoft partner in Application Development and Application Integration, a company must fulfil the following requirement:

• Four individuals from the organisation must pass one of the required certifications and be Microsoft certified. The certifications include MCDS: App Builder, Microsoft Certified: Azure Developer Associate, Microsoft Certified: Dynamics 365: Finance and Operations Apps Developer Associate, and Microsoft 365 Certified: Developer Associate.
• Validate required exams and skills each year
• Provide five customer references that validate and verify an exceptional level of delivery of solutions or services
• Pay the fee for the gold partnership
• Earn the partner points required (120)
• Pass in licensing overview assessment
• Pass in sales and marketing assessment
• Be a member of the Microsoft Partner Network (MPN)

As it’s quite clear, it’s not an easy feat to gain the gold Microsoft partnership, therefore, when you’re looking for a partner in software development it will help you differentiate between organisations and drive the elimination process.

Ardent and Microsoft over the years

Ardent’s data and software engineers have worked with a wide range of Microsoft technologies, ranging from leaders in the market such as Azure, SQL Server and .net to newcomers such as Power Apps, Power BI, Power Automate, and Microsoft Dynamics to name a few. We leverage Microsoft technologies every day to deliver innovative, robust application development, seamless cloud migration and more.

Ardent – Certified Microsoft Partner

At Ardent, we are proud to have achieved the gold level certification in Application Development and Application Integration. We want to continue to deliver excellence with Microsoft so we are investing further in other partnerships with Microsoft including Cloud Partnership which focuses on helping clients modernise their infrastructure and migrate applications and data to the cloud.

Our engineers are dedicated to delivering innovative solutions with world-leading Microsoft technologies. If you are looking for a credible partner for your next software project, then get in touch today to see how we can help.

The post Securing Microsoft Gold Partner status and what it entails appeared first on Ardent.