Managed data 

In order to provide users with ready and curated datasets, developers can make these datasets ready for use in advance. Relationships between tables and table aggregations can be defined in advance. At this stage, business rules can also be applied and company-specific metrics can be defined. By using this “managed data”, data used by multiple business units will always match. Looker always displays live data; therefore, there are no local copies of data at the end users.

This does not mean that end users are dependent on the datasets that developers create: users have the freedom to explore data themselves and create their own visualizations. To get a full grip on the data, the user is always able to zoom in from Looker to row-level data.

Sharing of information 

When users want to share data with colleagues, they can simply share a URL so that they can access a visualization or image. It is also possible to schedule automatic reports, which can be sent by e-mail or placed on an external FTP server. Of course, reports can also be stored locally by a user. This way you are flexible in the way you report and share information.

Subscription-based 

Because Looker is subscription-based, it is also easy to get started: you pay for the number of users that have access to the platform and you do not need to buy a large software package for the number of users that will be working with the software in the coming years. So it’s easy to get into Looker with a small team and then scale your subscription as analytics begins to play a larger role in your business. If after some time it turns out that Looker is not the right tool for you, or that it is not yet the right time for implementing analytics in your company then you can simply cancel this subscription.

Connectivity 

Looker can retrieve data from many different databases, such as Snowflake, MySQL, postgresql and MS SQL Server, as well as other databases (both local and cloud-based). So Looker is also a suitable choice if a data warehouse is currently hosted locally.

As a partner of Looker, we would like to give you insight into the possibilities and advantages of analytics in the cloud:

• Hosted in the cloud, so no worries about local installations or hardware performance
• Possibilities for managed data and curated datasets
• Queries directly on actual data: no local copies of datasets with end users
• Subscription-based

With data warehousing, all company data comes together in one place, providing analysts with an easy way to access all the data they need to provide the company with the right information. Starting with data warehousing can be a costly affair because an investment in hardware and software has to be made in advance. A traditional data warehouse must then be maintained and expanded at regular intervals to meet the growing flow of information and computing capacity to enable all analysts to perform their analyses in an acceptable amount of time.

cimt is partnering with Snowflake to not only provide customers who want to start data warehousing without a large investment, but also to provide customers who already own a data warehouse and want to take advantage of the best performance in the cloud.

Pay per use

Snowflake only charges for the storage and computing capacity that is actually used. Through so called warehouses – interactions with databases – analysts can specify how much computing capacity is needed to execute a particular query in order to shorten the turnaround time. The size of these warehouses varies from X-Small to 3X-Large, with the processing time halving for each step higher than the previous one. The choice of warehouse size can be changed if necessary while a query is running. Billing is only done based on the size of the warehouse and the time (per second) it was used. The structure of the system allows multiple analysts to run queries simultaneously and ETL processes to be run concurrently without affecting the speed of the system. This type of scalability is unique to Snowflake.

Communication and security

Data is always stored encrypted on Amazon or Azure servers and communication between the server and the client is always secure; there is no alternative. As a result, data is always secure. Snowflake has servers in Germany and Ireland, among others, so the storage of this data is covered by European legislation. Communication with the databases takes place through ODBC and JDBC connections, but many platforms such as Talend, Informatica, Fivetran, Looker, Qlik and Tableau also have pre-configured connections.

Management and Administration.

A data warehouse in the cloud can be set up and integrated in a very short time, so the Time-To-Benefit can be significantly shorter than with on-site solutions. In addition, the Snowflake platform requires no database administration. Indexes are automatically created and optimizations (tuning) applied, so users can focus on the data, rather than the database administration. In addition, the Time Travel feature exists, which allows for quick and easy undoing of, for example, the deletion of a table or entire database.

As a professional partner of Snowflake, we are happy to give you insight into the possibilities and advantages of a data warehouse in the cloud:

• Start data warehousing in the cloud with only a short implementation time and without investment in (software) licenses and hardware
• Unprecedented performance, even with simultaneous computations
• No need to worry about database administration or tuning
• Only pay for the storage and computing power you use, charged per second
• A data warehouse that you can easily let grow along with your company

Extract, Transform and Load (ETL) technologies are an essential part of data warehouse and data integration projects. For the replacement or renewal of legacy environments in this area, we see a growing demand for state-of-the-art and high-performing ETL solutions. Often in the context of migration projects, moving existing logic is a high barrier.

To accelerate the conversion and migration of an integration platform, we partner with Gamma Systems, a leading specialist in the field of automated conversion of existing ETL logic.

Why use a conversion tool? The benefits are shorter lead times during migration and lower development costs. With Gamma Studio, 60-80% of ETL code can be migrated to the new platform in an automated and ready-to-use manner – and this for the most widely usedETL toolings such as: Talend, Informatica, DataStage or SSIS. Mapping including metadata can be extracted and reused in the Gammadata warehouse studio.

ETL & DWH migration

Gamma’s Data Warehouse Studio is a development platform to accelerate data warehouse and data integration projects by an average of 50-70%. Design and implementation of a data warehouse are based on meta data. Reverse engineering of models from source systems or model-to-model conversion of a DWH business layer. With the help of a graphical user interface, software architects, information or business analysts can directly participate in the development of a data warehouse or BI project. Business rules, data mappings and coding patterns can be used to define the data extraction. Afterwards it is almost completely generated as SQL or ETL code and manual programming is therefore almost no longer necessary.

The most important features are:

• Model-driven platform for automated construction of a DWH
• ETL and SQL code generation based on templates
• Process management framework
• Automated generation of documentation, reports and data lineage
• Code generation patterns

Gamma’s Data Warehouse Studio integrates with the most widely used database, ETL, and big data technologies, including:

• Informatica
• DataStage
• Talend
• Netezza
• Teradata
• Snowflake
• Microsoft SQL Server
• Oracle
• DB2
• PostgreSQL
• Amazon RedShift
• XtremeData
• MySQL
• Hive