Mit Data Governance werden Nachvollziehbarkeit, Qualität und Schutz von Daten sichergestellt.

Big Data, Data Lake & Data Warehousing

When collecting and storing Big Data, we decide whether Data Warehouse or Data Lake is the right platform for you based on your requirements.
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What is Big Data?

Big Data refers to particularly large volumes of data that are collected, stored, processed and analyzed from a wide variety of sources.

They are characterized by their size, complexity, fast-moving nature and weak structuring. Big Data plays a particularly important role in the context of Business Intelligence (BI). In analyzing collected data, BI makes use of systematic approaches and, in particular, new software solutions for evaluating large volumes of data and improving the speed of data processing. 

Date Warehouse vs. Data Lake – Platforms for storing Big Data

When it comes to storing Big Data, the terms Data Warehouse and Data Lake have established and are suitable in different ways depending on the requirements. 

 

Data Warehouse

Data Warehouse captures structured data in mostly tabular form that has been processed for a specific purpose.
The data model is therefore predefined, which results in less flexibility of new data sources, but at the same time data in a Data Warehouse is very robust and easy to maintain. One approach to modeling Data Warehouses is the Data Vault.

Data Lake

Data Lake, on the other hand, captures both structured and unstructured data whose purpose has not yet been defined.
Since the data model is not defined until it is read, this is a potential source of error. In contrast, Data Lakes are very flexible and scalable.

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A selection of our customer projects

In numerous customer projects, we have successfully used Data Warehouse or Data Lake to store Big Data. Read the reference stories here:

Referenz 25.10.22

Interactive online portal identifies suitable employees

TIMETOACT digitizes several test procedures for KI.TEST to determine professional intelligence and personality.
Referenz 27.01.22

Talend migration in record time

TIMETOACT migrated the Talend Data Integration Suite, including all workflows and processes, to the internal system environment of the municipal utility (with a subscription license) within a very short period of time and will continue to provide support as a support partner in the future if required.
Referenz 29.03.21

vdek introduces new Data Warehouse solution

The TIMETOACT GROUP supports the Verband der Ersatzkassen e. V. in the switch from a system environment to the latest IBM technology.

Data Warehouse or Data Lake?

Which platform suits you better?

Whether Big Data, Data Lake, or Data Warehouse – the focus is always on the collection and storage of company-wide, sometimes mission-critical data. Initially, the technology should not be in the foreground. Instead, the right architecture and (combination of) technologies are selected based on the business requirements and restrictions. 

Based on these individual requirements, we decide what is better suited for your project: 

Data Structure

Structured data can be stored and analyzed excellently in relational databases, whereas other technologies can show their advantage with unstructured data.

Power

Giga, tera or petabyte? Response times in seconds or minutes for dozens or thousands of users?

Stability

At the latest, when the analytic solution is used to support enterprise-wide decision-making, failure must be minimized, backup must be assured, and scalability must be ensured.

Costs

Make or buy, Cloud or on-premises, scalable or "small footprint" - every decision must also be evaluated in terms of cost.

Procedure for the implementation of Big Data, Date Warehouse and Date Lake

  • Determine requirements based on previous and future requirements.

    Either we determine the requirements together with you in the Data Warehouse / Big Data Workshops and design an architecture, or there is already a preexisting one? If necessary, we also carry out a modernization of your platform.

  • Development and implementation of a functional platform.

    Depending on the architecture on-premises / SaaS / IaaS we design the platform and implement it.

  • Modeling, construction and Data Integration

    After the technical provision, we move on to the business implementation: modeling of the data model, creation of the Data Integration processes, etc.

  • Ongoing support of the project

    Whether jointly developed in workshops, or specified; whether realization by you, us or third parties.

Our services on Big Data, Data Warehouse und Data Lake

We are committed to finding and implementing the best technology for our customers' applications. To do this, we use a concept of workshops that build on each other.

Expertise

A team of experienced project managers, DHW architects and experts design (if no solution concept is yet available) and develop the complete analytical solution

Technical provision

Whether SaaS, IaaS or on-premises: We carry out the provision of services in the Hybrid Cloud for our customers. Depending on the requirements, we develop the perfect approach and provide the technical basis.

Operation and support

Numerous customers already rely on our expertise when it comes to ensuring the operation of the analytical platform. With our trained specialists, we ensure the operation of the systems and accessibility.
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The right vendor for every project:

Are you looking for technical support on Big Data, Data Warehouse and Data Lake? We work with OpenSource technologies and commercial solutions from the following vendors:

IBM

In many aspects of data warehousing, but especially in data lakes and noSQL databases, open source tools play an important role: Spark, Hadoop, MongoDB to name a few. Depending on your requirements, we also use other open source technologies.
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Microsoft

Relational IBM databases such as Db2 / Db2 Warehouse or Netezza Performance Server, or Object Storage are platforms for data warehouse or data lake solutions.
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Google Logo

Google

With SQL Server for on-premise, or various databases on Azure incl. DataBricks and Data Lake Storage, Microsoft offers technologies for the development of data warehouses and data lakes.
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Snowflake

The Oracle database has been considered a powerful platform for analytical applications for many decades and is used by us in data warehouse projects.
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Oracle

The Oracle database has been regarded as a powerful platform for analytical applications for many decades and is used by us in data warehouse projects.
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Open Source

Open source technologies play an important role in many aspects of data warehousing, but especially in data lakes and noSQL databases: Object Stores, Spark, Hadoop, Kafka, Postgres or MongoDB, to name just a few. Depending on your requirements, we also use other open source technologies.
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Contact us now!

We would be happy to advise you in a non-binding conversation and show you the potential and possibilities of Data Lake, Data Warehouse and Big Data. Just leave your contact details and we will get back to you as soon as possible.

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Martin Clement
Teamleiter Analytics TIMETOACT Software & Consulting GmbH
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Data Governance

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Companies can tailor their process support needs by using existing in-house IT landscapes in combination with different cloud enterprise solutions and cloud infrastructure services. We would be happy to advise you on your options in the area of hybrid cloud IT.
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Data Vault Modeling Approach

Data Vault is a modeling technique that is particularly suitable for agile Data Warehouses. It offers high flexibility for extensions, complete historization of data and allows parallelization of data loading processes. Data Vault supports without significantly increasing the complexity of the Data Warehouse over time.

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