Data Engineering and Consulting Services

As a data engineering consulting services  provider, we help businesses  keep data clean, rich, high-quality and well-structured.  Our customers  extract valuable business insights and use them for agile decision-making.

Migration to Cloud
Big Data
Storage
Processing
Programming
AWS
MS Azure
IBM Cloud Pak for Data
IBM Biglnsights
Haboop
Spark
HBASE
MongoDB
Cassandra
DB2 Woc
Cloudant
IBM COS
Elastic
Sqoop
Flume
HIVE
Kafka
Cloudera Impala
NIFI
IBM MQ
Python
Java
Scala
API
HTML5
Angular
React

Our Data Engineering Services

Try Visual Flow – an open source low to no code ELT tool for your data project

Benefits business reaps with data engineering service

1

Best data automation practices

Our engineers use the sharpest tools on the shelf to make the process of handling data as fast and accurate as possible. We implement data quality solutions, data cleansing practices, and a wide variety of other tools to retrieve, transfer, and store information without causing the customer any additional hassle.

2

Fully customizable data storage

No matter what your data storage needs, we can meet them. Get a fully customizable storage solution perfectly adapted to your business requirements from a trusted data engineering provider.

3

Excellent data processing scalability

With advanced enterprise big data solutions, our engineers can set up the processing of virtually any volume of data.  Make the most of continuous information analytics with our data engineering and consulting services.

4

Seamless operation

We ensure  that during data aggregation, migration, and other information-handling activities, none of your business processes will stop for a moment. Keep operations running smoothly while the data is working for you.

5

Improved efficiency

Our engineers integrate AI, Big Data, and Data Visualization tools into your business processes for the most efficient operation. The perfect  cutting-edge technology will be at your disposal.

6

Reduced costs

Optimize your expenses by adjusting infrastructure TCO, speeding up infrastructure growth, and reducing time-to-market. Our consulting team’s comprehensive work will help you significantly reduce data processing costs and increase revenue.

1

Best data automation practices

Our engineers use the sharpest tools on the shelf to make the process of handling data as fast and accurate as possible. We implement data quality solutions, data cleansing practices, and a wide variety of other tools to retrieve, transfer, and store information without causing the customer any additional hassle.

2

Fully customizable data storage

No matter what your data storage needs, we can meet them. Get a fully customizable storage solution perfectly adapted to your business requirements from a trusted data engineering provider.

3

Excellent data processing scalability

With advanced enterprise big data solutions, our engineers can set up the processing of virtually any volume of data.  Make the most of continuous information analytics with our data engineering and consulting services.

4

Seamless operation

We ensure  that during data aggregation, migration, and other information-handling activities, none of your business processes will stop for a moment. Keep operations running smoothly while the data is working for you.

5

Improved efficiency

Our engineers integrate AI, Big Data, and Data Visualization tools into your business processes for the most efficient operation. The perfect  cutting-edge technology will be at your disposal.

6

Reduced costs

Optimize your expenses by adjusting infrastructure TCO, speeding up infrastructure growth, and reducing time-to-market. Our consulting team’s comprehensive work will help you significantly reduce data processing costs and increase revenue.

Frequently asked questions

What is the difference between ETL and ELT?

Both ETL and ELT are abbreviations to describe steps of the data integration process, and the difference in the letter sequence reflects the difference in the sequence of these steps.

ETL stands for extract, transform, load. ETL jobs and pipelines help move data from source to target, taking into account DWH design. Data transformation occurs in the processing area, and processed information that meets standards, such as GDPR, HIPAA, etc., enters the target systems.

ELT stands for extract, load, transform. With ELT processes, data is loaded into Data Lake or target systems and is processed after loading. This approach provides more flexibility and simplifies storage when new data formats evolve.

Is ELT an alternative to ETL?

ELT can be seen as an alternative approach to data transformation. The Extract-Load-Transform approach is seen as a more modern and agile way to work with data than ETL. Data analysts can see all the data when loaded to the warehouse and can participate in setting data transformation logic. In this case, transformed data will be truly representative. Working in a data engineering company, we noticed that ELT process is faster to perform and avoids server scaling issues.

What are the challenges of big data?

Despite being a game changer in the business world in a digital era, big data creates challenges at all stages, from collecting to maintenance and deriving insights. Here are the key challenges of big data:

  • Weak understanding of massive data. Generally, employees have a limited understanding of what data is, considering it as mere numbers in reports or some personal information. The average employee can’t see how each business process creates data by the essence, which makes it hard to understand how to make use of it.
  • Integrating and managing multiple sources of data also seems to be problematic.
  • Inaccurate analytics and difficulties with getting insights.
  • Choosing big data tools and incorporating them into the business software ecosystem.
  • Data breaches and security issues.
  • Lacking of big data experts.

Visual Flow data engineering services provider can help you to overcome these challenges by offering winning ELT/ETL tools and high-standard big data engineering services.

Why migrate to the cloud?

The cloud data engineering services provide multiple engagement models to match your budget and help to reduce fixed costs and total cost of ownership. This helps speed up infrastructure growth and introduce new capabilities (AI, Data Visualization, and more).

How does cloud migration work for you?

Cloud migration with IBA involves a three-step process.
Step 1 Mobilization. Defining migration goals, scope, risks, and model of work (Scrum/Kanban).
Step 2 Initial Iteration. Final approval of terms, scope, team, milestones, goals, etc. for MVP.
Step 3 Pilot Iteration(s). Iterative /continuous process with discussions about unplanned changes.
Step 4 Presentation of MVP. Correction of the high-level plan.

How long does a migration to the cloud take?

With IBA Group as your cloud migration partner, we’ll prove the value of the cloud and jumpstart your broader migration. In about two months, you will:

  • get your MVP application in the cloud,
  • get experience planning & migrating an application to AWS,
  • get a comprehensive migration assessment with an indication of your projected, optimized in-cloud cost,
  • be ready to continue.
What are the factors that influence migration costs?

The final amount depends on a range of factors, such as:

  • The complexity of the current IT infrastructure (e.g., warehouses and database size – how many schemas and tables should be migrated);
  • The data volume to be migrated;
  • The chosen migration strategy; e.g., rehosting requires fewer efforts than refactoring, therefore it’s less expensive;
  • The current IT infrastructure’s migration readiness;
  • The extent of migration automation and the cost of migration automation software licenses;
  • Testing coverage.

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