Big data: solutions for volume, variety and speed
Many companies have been analyzing external and internal data extensively for a long time. However, additional technology must be used in many areas to overcome the challenges posed by large and rapidly changing, often polystructured data volumes. This is because big data does not just mean a different dimension of data volumes, but big data stands for:
- Volume: Enormous amounts of data require scalable systems
- Variety: Different data formats must be integrated
- Velocity (speed): Real-time analyses are crucial
Volume: The exponential growth of data
According to Wikipedia reported a global data volume of 60 zettabytes (60 trillion Gigabytes) in 2020. and grows by 25% annually. This enormous amount of data could form a stack of Blu-ray discs that exceeds the weight of the Giza pyramid.
While social media data such as from Facebook and Twitter is often cited as an example of big data, the more relevant information is usually found in internal and external data sources. However, detailing, combining and historicizing this data often leads to volumes that are difficult to manage. The segmentation, partitioning and prioritization of data sets is therefore crucial.
Industry 4.0 and the Internet of Things (IoT) are further increasing data growth and presenting companies with major challenges.
Challenges for companies:
- Handling large amounts of data
- Efficient data organization
- Integration of new technologies
Diversity: Dynamics and flexibility in data processing
The multitude of technical solutions, globalization and rapid system changes lead to constantly changing sources of information. Relational databases expect pre-structured data, while big data solutions are flexible and only structure data during analysis (ELT instead of ETL). Modern databases process formats such as JSON using NoSQL modes, often without introducing new technologies.
Challenges for companies:
- Integration and adaptation to changing data sources and formats
- Flexibility in structuring and processing data
- Efficient use of existing databases and technologies
Companies need to react flexibly to dynamic data sources and make optimum use of existing systems in order to manage the diversity of big data effectively.
Velocity: Fast decisions through Data Lake
Within seconds, analyses on data lakes deliver valuable insights, such as on investments, target groups, or process optimizations. However, traditional data warehouse systems quickly reach their limits when handling large volumes of data. A combination of traditional, in-memory-based, and batch-processing systems can overcome this challenge. At the same time, it is crucial to focus on relevant information to ensure efficiency.
Challenges for companies:
- Processing and analyzing large amounts of data in real time.
- Integration of new technologies with existing systems.
- Focus on the essential and relevant information.
Companies need to optimize existing systems, integrate real-time solutions and focus on relevant data in order to use speed as a competitive advantage.
Data Lakehouse – Cost-effective architectures
Data lakehouses not only help you gain real-time business insights, but can also help you reduce costs. Lower your storage, computing, and licensing costs by avoiding data duplication and ETL processes compared to typical data lakes. Data warehouse architectures.
Data lakehouses are often based on open formats such as Apache Iceberg.
Our consulting services start right here: We would be happy to conduct feasibility studies and free consultations with you on the topics of Big Data, Data Lakehouse and related subjects.
Recognize potential
We make the methods and potential of Big Data and AI tangible – e.g. through Potential workshops or Proof-of-concepts.
Concept
We support you in the conception of your projects – from idea generation using Design Thinking, through the structured Development of a use case to the prototype and the fully automated processes.
Implementation
We support or take over projects From start to finish – from prototyping and technical concept development to project management – and we also optimize existing systems. Furthermore, we improve your big data systems both technically and methodologically.
Tools
- Hadoop
- Impala
- Oracle Exadata
- IBM Watson
- Talend ETL
- Kafka
- Apache Drill
- Hive
- Apache Iceberg
- Parquet
- Spark (in certain contexts also as a method)
- MinIO / S3
Methods
- Agile
- Scrum
- DevOps
Discover your potential!
We help you use large amounts of data efficiently. Big Data technologies such as distributed systems, real-time analytics and AI allow you to gain insights, optimize processes and make informed decisions – scalable and future-proof.
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