Data ware­hous­ing solu­tion’s jump to cloud

Nowa­days, data ware­hous­ing solu­tions large­ly rely on cloud resources. At Islet, a large por­tion of new solu­tions are imple­ment­ed as Lake­house solu­tions, either on top of Synapse Ana­lyt­ics or Data­bricks. The imple­men­ta­tion is deter­mined based on require­ments, and the data ware­hous­ing solu­tion is cho­sen based on which tech­nol­o­gy best ful­fills these requirements.

In rare cas­es, the use of cloud ser­vices is ini­ti­at­ed by bring­ing along part of the old solu­tion. These few cas­es are, for exam­ple, those where the orig­i­nal solu­tion is so broad or impor­tant to main­tain con­ti­nu­ity of oper­a­tions. We will go through the ways in which the tran­si­tion to the cloud can be made by bring­ing along part of the old solution.

Ben­e­fits of cloud

In this post, we will go through ways to start using Microsoft­’s cloud ser­vices eas­i­ly and effort­less­ly in scope of data and ana­lyt­ics. Cloud ser­vices offer sev­er­al ben­e­fits, such as flex­i­bil­i­ty, per­for­mance, and cost-effectiveness.

Flex­i­bil­i­ty is one of the most sig­nif­i­cant ben­e­fits of cloud ser­vices. Microsoft offers var­i­ous ser­vices and solu­tions that can be scaled accord­ing to your needs. You can eas­i­ly increase or decrease capac­i­ty and resources in line with your busi­ness require­ments. This gives you flex­i­bil­i­ty and the abil­i­ty to adapt quick­ly to rapid­ly chang­ing busi­ness conditions.

Per­for­mance is anoth­er impor­tant advan­tage that cloud ser­vices pro­vide. Microsoft­’s cloud ser­vices are designed to pro­vide high per­for­mance and effi­cient use of resources. You can lever­age the scal­a­bil­i­ty and per­for­mance offered by cloud ser­vices when deal­ing with large amounts of data or per­form­ing heavy com­pu­ta­tion tasks. This ensures the effi­cien­cy and speed of your busi­ness operations.

Cost-effec­tive­ness is the third crit­i­cal fac­tor that attracts busi­ness­es to use cloud ser­vices. Microsoft­’s cloud ser­vices offer flex­i­ble pric­ing mod­els where you pay only for the resources used. You don’t need to invest a large ini­tial cap­i­tal or main­tain expen­sive infra­struc­tures. Along with scal­a­bil­i­ty, cloud ser­vices also offer the oppor­tu­ni­ty to opti­mize costs and pay only for the ser­vices you need.

Tra­di­tion­al Data Ware­house Solution

A tra­di­tion­al data ware­house solu­tion imple­ment­ed with Microsoft prod­ucts is usu­al­ly based on the SQL Serv­er, SSIS pack­ages for data trans­fer, and sched­ul­ing with SQL Serv­er Agent.

SQL Serv­er is a well-known and reli­able data­base solu­tion that pro­vides an effec­tive foun­da­tion for a data ware­hous­ing environment.

SSIS (SQL Serv­er Inte­gra­tion Ser­vices) is an ETL (Extract, Trans­form, Load) tool pro­vid­ed by Microsoft, which sim­pli­fies data trans­fer between dif­fer­ent systems.

Sched­ul­ing with the SQL Serv­er Agent pro­vides automa­tion and sched­ul­ing in the data ware­hous­ing environment.

This com­bi­na­tion of SQL Serv­er, SSIS pack­ages, and SQL Serv­er Agent has pro­vid­ed a sol­id foun­da­tion for data man­age­ment and analysis.

How do we pro­ceed from this tra­di­tion­al solu­tion towards the cloud?

The first step is to decide which parts of the cur­rent data ware­house you want to bring to the cloud. You can choose a cer­tain or all parts depend­ing on your needs and busi­ness objec­tives. It’s impor­tant to ensure and dou­ble check that the solu­tions to be trans­ferred to the cloud are com­pat­i­ble with the new envi­ron­ment so that the tran­si­tion is seamless.

A tra­di­tion­al data ware­house solu­tion imple­ment­ed with Microsoft prod­ucts is well-suit­ed for a lift-and-shift type of cloud migration.

By mov­ing an SQL Serv­er data ware­house locat­ed on your own servers, as well as the SSIS runs, to the Microsoft­’s Azure SQL Man­aged Instance, the old solu­tion can be migrat­ed with as lit­tle effort as pos­si­ble, while also enabling the devel­op­ment of new cloud-native solutions.

Azure SQL Man­aged Instance pro­vides near­ly 100% com­pat­i­bil­i­ty with local SQL Serv­er, which facil­i­tates the tran­si­tion from local data ware­hous­es to the cloud. In addi­tion, exe­cut­ing SSIS pack­ages in Azure Data Fac­to­ry allows exist­ing ETL process­es to be used with min­i­mal changes.

Cloud-based data ware­hous­es offer a more cost-effec­tive way to scale resources as need­ed. Cus­tomers can reduce costs by mov­ing from acquir­ing hard­ware used by the data ware­house to only pay­ing for the resources used in the cloud. This avoids com­mit­ting sig­nif­i­cant costs… for exam­ple every four years, and costs can be mon­i­tored and adjust­ed accord­ing to the resources used. Most resources in the Azure cloud offer scal­a­bil­i­ty, as well as the option to shut down resources when they are not in use.

Azure SQL Man­aged Instance pro­vides scal­able per­for­mance com­pared to a local data ware­house. Scal­able resources ensure that sys­tem capac­i­ty adapts to cus­tomer needs and enables effi­cient work with large amounts of data. Rarely there are sit­u­a­tions where the resources offered by Azure would no longer pro­vide scal­a­bil­i­ty. The archi­tec­ture must always con­sid­er the nature of dif­fer­ent resources, the usage require­ments of the solu­tion, and there­by make the right resource choices.

With cloud ser­vices, cus­tomers can take advan­tage of advanced resource secu­ri­ty that are con­tin­u­ous­ly updat­ed along with the ser­vice. In this case, the upkeep of keep­ing updates up to date is trans­ferred to the cloud ser­vice provider. Too often, local solu­tions have lagged in updates or have even fall­en out­side of updates due to out­dat­ed oper­at­ing systems.

Con­tin­u­ous Devel­op­ment of Cloud-Native Solutions

The solu­tion does­n’t usu­al­ly stop when moved to the cloud. This is only the first step towards cloud-native devel­op­ment. To enable fur­ther devel­op­ment, you can cre­ate new envi­ron­ments and use the ser­vices offered by the cloud even more efficiently.

When cloud migra­tion reach­es the point where rapid tran­si­tion to the cloud has been achieved based on the orig­i­nal solu­tion, then we need to con­sid­er how the devel­op­ment of the new solu­tion will be imple­ment­ed. In this case, it makes sense to con­tin­ue with a cloud-native solu­tion, which means grad­u­al­ly mov­ing away from an SQL Serv­er and SSIS-based solution.

Below is an exam­ple of the ben­e­fits of resources used for cloud-native devel­op­ment, for exam­ple if Azure Synapse is cho­sen for cloud-native development.

1. Mas­sive scal­a­bil­i­ty: Data Lake-based solu­tions are high­ly scal­able, enabling the stor­age and pro­cess­ing of vast amounts of data. Azure Synapse, togeth­er with Azure Data Lake Stor­age Gen2, pro­vides high per­for­mance and scal­a­bil­i­ty for both struc­tured and unstruc­tured data.
2. Flex­i­bil­i­ty: Azure Synapse’s Data Lake solu­tion accepts var­i­ous data types and for­mats, such as text files, images, videos, and JSON files. This flex­i­bil­i­ty enables the han­dling and inte­gra­tion of com­plex data struc­tures, allow­ing com­pa­nies to ana­lyze a wider range of data sources.
3. Cost-effi­cien­cy: Data Lake-based solu­tions like Azure Synapse offer an afford­able stor­age option for large amounts of data. Stor­age costs remain low because you only pay for the stor­age space and com­put­ing resources used.
4. Data man­age­ment: Azure Synapse and Data Lake Stor­age Gen2 togeth­er offer an effec­tive data man­age­ment solu­tion. A hier­ar­chi­cal stor­age struc­ture and meta­da­ta man­age­ment enable eas­i­er clas­si­fi­ca­tion, retrieval, and orga­ni­za­tion of data.
5. Data secu­ri­ty and com­pli­ance: Azure Synapse includes advanced secu­ri­ty fea­tures such as data encryp­tion at rest and in tran­sit. In addi­tion, it adheres to strict data pro­tec­tion stan­dards, such as GDPR, help­ing com­pa­nies ensure data pro­tec­tion and compliance.
6. Real-time ana­lyt­ics: Azure Synapse and Data Lake solu­tion enable real-time ana­lyt­ics in han­dling large amounts of data. This allows for quick insights and sup­ports data-dri­ven deci­sion making.
7. Com­pat­i­bil­i­ty and inte­gra­tion: Azure Synapse is com­pat­i­ble with many cloud ser­vices and tools, includ­ing Azure Machine Learn­ing, Pow­er BI, and Azure Data Fac­to­ry. This facil­i­tates data flow man­age­ment, ana­lyt­ics, and visualization.

To enable cloud-native devel­op­ment, it is cru­cial to cre­ate new envi­ron­ments and uti­lize cloud ser­vices such as Azure Synapse and Data Lake. With these, you can con­tin­ue to devel­op your solu­tion, scale resources as need­ed, and lever­age the full ben­e­fits offered by the cloud.

Microsoft also offers oth­er cloud-native devel­op­ment mod­els to sup­port data and ana­lyt­ics solu­tions. You might see some­thing about those in our Islet blogs… stay tuned!

Best regards,

Mikko and Ilkka

PS. If inter­est­ed, read more about our Data & Ana­lyt­ics ser­vices here