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• Some functions are considered outdated and only work through the ‘Classic’ portal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

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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.

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Tell us about what you do at Ardent

At Ardent, I am responsible for designing and developing the windows, web, and mobile app solutions for one particular client we work with on an ongoing basis. I am also responsible for troubleshooting, debugging, and upgrading the existing software for a number of other clients. I usually work alongside the project manager and other developers to develop project activities, schedules, and regular project progress reports.

What does your typical day look like?

My day starts with my essential item of the day, a cup of tea. Then, once I have reviewed some critical emails, I usually catch up on any communications from the previous day. In the next 10-15 minutes we have a stand-up meeting where everyone says what they’ve achieved, what they’re struggling or blocked with (if anything), and what they’re going to be doing that day.

After the stand-up, if needed, I spend some time with the team working out any issues they may have and then move on for a solid hour of coding which involves problem-solving, development, troubleshooting production issues etc. To close out my day, I try to finish my open items, code reviews and the project deliveries, if any.

How did you find the office environment when you first started?

Things have changed a lot since I joined Ardent. Ardent has invested in many programs to benchmark itself against industry best practices. Some of the noticeable programs include the ‘Buddy up scheme’ where every new joiner is partnered with a buddy from the beginning so they are supported from day one, regular training and world recognised certifications in order for the employees to serve clients with confidence, software audits to check its quality, progress, or adherence to plans, standards and regulation.

What do you most value about the Ardent culture?

When I joined here as a junior software developer, I was surprised to learn how much communication and teamwork were required – not just within your own team but also the indirect teams. This collaboration is demonstrated highly within the culture of Ardent. I know I can contact any person within the company, and they would be willing to help with any query I may have.

One of the things I love in Ardent is having the ability and freedom to investigate new emerging technologies to find the best fit for the given requirement. These investigations give me the chance to upskill and gain experience to better my decisions in the future.

What’s something you are most proud of achieving since you have been at Ardent?

Over the past few years we have developed and successfully launched many android and iOS apps which is something I am proud of. It’s exciting to see customers using something we only initially imagined in our minds. Making an impact in the industry and in customers’ lives is motivating and to be proud of.

What are you most looking forward to?

“Every day brings something new to learn and improve.” Since joining Ardent, I have worked on several projects ranging from windows, and web applications to building cross-platform mobile apps. I am consistently looking forward to improving the performance and stability of these apps to ensure a great customer experience.

What led you to a career in tech?

The highly important factor which led me to have a career in tech is: “A big part of your career will involve helping people”. Whether you’re working on tech support to solve everyday challenges or developing solutions behind the scenes, a big part of your career will involve making other people’s lives simpler with your specialized knowledge. Tech is truly a rewarding career, where you leave every day with the satisfaction that you did some good, either in the lives of your colleagues or in some corner of the world.

How did you find your way to Ardent?

When I was ready to take on a new challenge and join a bigger team, I found Ardent which offers great work culture and value system. I found that the job description fitted well with my skills and qualifications and saw the opportunity to grow my abilities and expand prospects for career advancement. With a good level of experience and skills behind me, I was optimistic about adding immense value to the position Ardent offered me.

Who are you when you’re not at Ardent?

I am a person who always loves cooking. Cooking is something I find enjoyable, as the food is the one that brings people together. Inviting a few relatives and friends over to enjoy an evening together around food can be truly satisfying. I am also a big foodie and a traveller, who can go on a road trip or travel far distances just to try a specific dish or trendy food and enjoy travelling.

We asked some of the team what it is like working with Deepa

Deepa is one of the best colleagues in the organization. She is always smiling and carries a positive approach wherever she goes. She is hardworking and is always ready to help anyone. She shows her excellence not only in work but also in the team-building events and games in which we all take part in. She is named the “Jenga Queen” of Ardent. – Anirudha Deshmukh, HR Manager

Deepa is a smart and working individual, he has excellent presentation skills which are helpful in project demos and training. She is a quick learner and has invested her time in learning a number of technologies. Overall she is an excellent development and a pleasure to work with. She always takes part in team-building activities from quizzes to festival days and we get to see her incredible Rangoli skills over the festival. – Laxman Amrale, Head of Data Engineering

Find out more about careers at Ardent or explore our other ‘Meet the team’ stories:

Meet the team: Noor Khan – Senior Marketing Executive

Meet the team: Vikas Bare – Associate Technical Account Manager

The post Meet the team: Deepa Mhatre – Senior Software Developer appeared first on Ardent.

Schema optimisation is an efficient tool for maximising the speed and efficiency of your database and allowing performance enhancement through design that addresses functional needs. In this article, we will look at why you need to optimise your schema and the benefits to gain from it.

How does optimising your schema benefit you?

With many platforms operating on pay-as-you-use payment methods to use the service, time is precious – and you do not want to be spending unnecessary time and money waiting for processes to complete.

Having a strategy for database optimisation can help to reduce the system response time, provide a clear and easily indexable or searchable structure, and allow for content to be extracted, migrated, or arranged with minimal resource wastage. To reduce performance issues that occur with overly complicated object models and database schema, optimisation is required.

What technology is used to effectively optimise schema?

To optimise a schema, various cloud platforms have tools available, including the likes of Azure SQL Database, or AWS’s Schema Conversion Tool. There is also the option on many platforms to manually improve database structure, this could be through index suggestions, removal of unused tables or columns, identifying and fixing invalid class mappings, reducing disk access, or improving caching.

What you need to consider before optimising your data schema

It is essential that before you start making changes or alterations to your schema, your data is backed up. Although this should be standard practice to avoid the loss of data and ensure data consistency, this is even more paramount when making significant changes. If your data is backed up and you run into any issues, or the optimisation does not produce the intended results, you can still recall your previous schema and undo any changes.

It’s important to remember that a de-normalised schema can speed up some queries but may slow down others, and although removing tables or columns may reduce space, they should be carefully checked to ensure they are not tied into any other operations that require the data to be in place.

Depending on the cloud platform you’re using for your data, there may be specific tools, access to knowledge bases, or advice available to support your optimisation process – it is highly recommended that you look at the available help and support before making changes to your schema, so you can identify exactly what processes your provider is using, what level of technical understanding you need to have to conduct the optimisation, and have access to troubleshooting should the process not go according to plan.

Continuous optimisation

Creating a well-designed data schema which will optimise the performance of your data is great, however, it will need consistency and continuous optimisation. Your database will only work as well as it is structured, and as your data sets evolve and grow regular optimisation and management will be required and should be a key element of your process.

Ardent data schema optimisation services

Our highly skilled data engineers have worked on a wide variety of data warehousing projects including the optimisation of their data schema. If you have low, declining performance or lack consistency in your data structure, Ardent can help. Our engineers can either optimise your existing schema structure or architect a sophisticated data warehouse to optimise your data performance, data accessibility and data speed. Explore our data warehousing service or get in touch to enhance your data performance.

The post What is schema optimisation and why do you need to optimise your schema appeared first on Ardent.

Hosting and making use of your data on a cloud service can make it much more accessible and provide extra security and functionality. More than likely your data will evolve and grow, therefore you may outgrow your existing data platform. In this article, we look at Oracle to Redshift data migration and what you need to consider.

What is the Oracle Cloud Infrastructure (OCI)?

Oracle is a public cloud platform, that has been developed to improve existing processes and to make data solutions more streamlined, functional, and accessible. The platform accelerates migrations of existing workloads and offers reliability and high performance.

Some of the services Oracle supports include: data migration, creating custom builds, developing new applications on the cloud, and running processes from existing workloads.

The pros and cons of using Oracle

Oracle has tools to make the data migration process easier, such as the Oracle Data Pump, this utility allows for very high-speed movement from one Oracle DB to another.

The platform also offers isolated network virtualisation, to prevent attacks, and has a custom-designed SmartNIC.

The autonomous guiding has been designed to be user-friendly, with fast loading, and helpful hints provided.

However, location-based access control can cause issues, and there isn’t as much of a knowledge base for support, compared to other cloud services.

Choosing to work with AWS Redshift

Redshift is a cloud service offered by Amazon and is known for performance innovation, secure and reliable data warehouse storage, predictive insights, and scalable analytics.

Redshift offers fully managed petabyte-scale data warehouse space, and the service is often said to be fast, scalable, and cost-effective.

Ardent is a certified AWS partner, read more on what that means for you.

The pros and cons of using Redshift

Redshift is known for providing fast-scaling and robust security tools, with ease of use and accessibility – which is also supported by a large knowledge base and assistance.

However, users are required to understand the distribution and sort keys for indexing data, and it doesn’t work as a live app database.

When it comes to migrating the data, your internal systems have to send information to the system or utilise USB drives to an AWS shipper – which can affect the ability to send over data to be warehoused, especially for small businesses who have capped data usage.

Why migrate your data from Oracle to Redshift?

Data migration comes about from improving data storage and retrieval, upgrading functionality, gaining confidence in using technology, or simply outgrowing an existing cloud platform. These may be just some of the reasons you may opt to move your data from Oracle to Redshift. However, migrating your data from Oracle to Redshift will offer benefits including:

• Improve data query and data load performance
• Increase data scalability to future-proof your data
• Save costs significantly

What are the key differences between Oracle and Redshift?

Oracle and Redshift have fundamental differences in the way the platforms operate and how they store data.

• Redshift offers data blocks in much larger sizes, and the data is stored separately in columns – allowing for different forms of data compression.
• Oracle has a wide range of database features, which are not all available to use in Redshift, and some data types can not be used in the same format across the two services.

Basis of migrating your data

Determining the functionality you need, your preferred method of operation, and how you want to use your data, will largely inform your decision to use either service, and whether or not to migrate your data to the new platform.

Ardent data migration solutions

We have worked on multiple projects migrating data from one platform to another whether it’s Oracle to Redshift data migration, AWS to Azure, vice versa and much more. Ardent data migration solutions give you peace of mind knowing your data is being handled by experts who have decades of experience dealing with a wide variety and large volumes of data. If you are looking for a credible and reliable data engineering partner with a proven track record, Ardent can help. Explore our data engineering services or get in touch to unlock your data potential.

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Many organisations will often use similar sort of technologies to their existing technology stack, for example, we have has clients that use AWS technology stack so have migrated their data to Redshift and vice versa with Microsoft Azure.

In this article, we are going to look at migrating data to and from AWS Redshift to Microsoft Azure – what you need to consider, the pros and cons of doing this, and how the services differ – so you can get a good idea whether this platform change is for you.

Redshift – storing and migrating your data

Redshift is used by customers to analyse data, run complex analytical queries, and manage data.

Redshift data migration is seen to be simple and effective, with the ability to scale the number of nodes in your warehouse in the console or with an API call. Redshift now provides native integration with the Microsoft Azure Active Directory (AD).

Redshift for your data migration – the benefits and limitations

Whilst Redshift is often seen as not being as cost-effective as some other cloud solutions, the platform has been praised for providing better support and security features than its rivals; and although the GUI is considered far too complex for first-time users, it generally provides a faster data processing speed than its competitors (including Azure).

A known issue, that causes delays in data migration speed, is having inadequate resources allocated to the replication instance and if multiple tasks are being run across Redshift (as an endpoint), then they are more I/O intensive.

Azure – storing and migrating your data

Azure is designed to provide a reliable platform between on-premises and public cloud, with a broad range of hybrid connections (VPNs, caches, CDNS, etc) and hosts over 200 products to manage data solutions.

The database migration tool allows for a simple, guided, and automated migration service, which allows data migration from multiple sources to the cloud, at scale.

Azure for your data migration – the benefits and limitations

Azure data migration can be done using the tools provided (such as the Azure Data Factory) and has been noted for allowing large data loads, control of the process, and effort / time saved on data partitions and solutions created for high resilience.
The connector for migrating from Amazon Redshift to Azure is a supported capability.

Some users have found issues with Azure include sorting out third-party integration points (such as mail servers, data processors, etc) and needing to use the separate Azure Integration Service tool to make the process easier and having to factor this into their data migration plans.

What are the differences between Redshift and Azure?

The two platforms differ in a number of key areas, which can make the services more or less suitable for your needs, depending on what you require after you have finished migrating data.

Redshift has clusters with ‘nodes’, and configurations that bundle CPU, memory storage, and IOPS – and they offer three types of ‘on-demand’ nodes (with different performance levels.
Azure, tends to price compute and storage resources separately with Data Warehouse Units (DWU) comprising of CPU, Memory, and IOPS being one price, and the storage at another rate.

To protect the data, Redshift automatically takes incremental snapshots that track changes to the cluster – it does this by default every eight hours, or every 5GB (per node) of data change (whichever comes first).

On the other hand, Azure takes automatic screenshots throughout the day (to create restore points which are available for 7 days) and allows users to trigger up to 42 user-defined snapshots.

What to consider before migrating your data?

Depending on the size of your data migration, you may need to integrate different tools or functions into your planning, and carefully evaluate both platforms to determine which is going to provide a better fit, and a more cost-effective, successful solution.

Effective planning of your data migration project is key, read the full article on how to plan your data migration.

Ardent data migration solutions

If you are considering migrating data from Redshift to Azure or are yet to decide on technology and are still exploring your options, our experienced data engineers can help. We have worked with a wide variety of clients, data and technologies to deliver data migration solutions quickly and efficiently. If you are looking to migrate your data from redshift to Azure or vice versa, our highly skilled data engineers can help. Get in touch to find out more or to get started.

The post Migrating Data from Redshift to Azure: 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.

There are two main types of data migration strategies. We will explore each one, the key benefits and limitations of each and how to choose which is the right strategy for your data and organisation.

Trickle database migration

Trickle migration is the migration of data in small increments, hence the name ‘trickle’. The data is broken down and organised in chunks to carry out the migration for each separately. Each chunk of data migration will have its own timeline and once completed the next mini-migration can begin. This ensures that should an error or failure occur the entire data migration is not impacted. Only, the data migration at hand is affected which makes dealing with these issues a lot more manageable for the data migration teams.

There are some key benefits and limitations to consider if you are looking to migrate your data with the trickle database migration.

Key benefits

• Manageable failure recovery
• No downtime is required
• Any lessons learnt can be applied to subsequent mini-migration
• Visibility across data migration stages

Limitations

• It may require a longer time with multiple mini migrations
• Users have to work with both systems to access their data during the migration process
• Can be more expensive to maintain both source and destination systems
• Can be more complex with the syncing of data

Big bang database migration

Big bang database migration is data that is migrated in one phase. The entire database is migrated from one system to another, therefore a significant level of preparation is key. To ensure minimal disruption to a business’s operations, designing, planning and testing are key. With this method there will be downtime of the system when the actual ‘bang’ data migration is taking place, this will vary depending on a wide range of factors including volume of data, variety of data, data source and destination and the technologies employed.

There are a number of key benefits and limitations of big bang database migration and they include:

Key benefits

• It can be in-expensive if all goes to plan
• One big migration can be simple compared to parallel migrations taking place
• The actual migration point is in a short time frame

Limitations

• If one error occurs it can impact the entire data migration
• This can result in extended downtime and high costs
• Downtime is required for both source and destination systems

Data migration strategies – Big bang database migration Vs Trickle database migration

Choosing the right data migration strategy is key to avoiding dealing with longer downtimes and costly failures. Therefore, you will need to take these factors into consideration:

• Are you able to have a downtime of your systems or do the systems need to be up and running to ensure data consistency?
• What is your budget? As big bang can be the more cost-effective solution if no major issues occur.
• What is the volume and variety of data, which will ultimately affect the duration and the strategy selected?
• How accessible does your data need to be during the migration process?

Ardent data migration solutions

If you are unsure about what data migration strategy is right for you, then it may be worth speaking to an experienced data engineering services provider to get an insight into what is best suited to your data and your organisation. Ardent has worked with a wide variety of clients to deliver data migration solutions with minimal disruption to day-to-day operations. If you want peace of mind whilst migrating your data with experienced professionals, then get in touch to find out more.

Read about migrating data from Microsoft to AWS and the key challenges of migrating data.  

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